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Background Patients with diabetes and non-ST elevation acute coronary syndrome (NSTE-ACS) have an increased risk of mortality and adverse outcomes following percutaneous coronary intervention (PCI). Purpose We aimed to investigate the impact of early, within 24 hours PCI compared with only routine medical treatment on clinical outcomes in a large international cohort of patients with NSTE-ACS and diabetes. Methods We identified 1,250 patients with diabetes and NSTE-ACS from a registry-based population between October 2010 and April 2016. The primary endpoint was 30-day all-cause mortality. The secondary endpoint was the composite outcome of 30-day all-cause mortality and left ventricular dysfunction (ejection fraction Results Of 1,250 NSTE-ACS first-day survivors with diabetes (median age 67 years; 59%, men), 470 (37.6%) received early PCI and 780 routine medical treatment. The overall 30-day all-cause mortality rates were higher in the routine medical treatment than the early PCI group (6.3% vs. 2.5%). The prediction results of the MC algorithms accounted for only one interaction term that was statistically significant: age ≥65 years. After propensity-matched analysis as well as IPTW, early PCI was associated with reduced 30-day all-cause mortality in the older age (OR: 0.35; 95% CI: 0.14 to 0.92 and 0.43; 95% CI: 0.21 to 0.86, respectively), whereas younger age had no association with the primary endpoint. Similar results were also obtained for the secondary endpoint. Conclusions Among patients with diabetes hospitalized for NSTE-ACS, an early, within 24 hours, PCI strategy is associated with reduced odds of 30-day mortality only for patients aged 65 years or over. MC algorithms provide accurate identification of treatment effect modifiers.

Social cloud systems, which aggregate the computing capabilities of a large pool of users, have emerged in recent years as a key solution for resource provision and sharing in large-scale online communities due to their inherent flexibility and cost-effectiveness. However, the performance and reliability of these systems depend on the users' cooperative behavior in sharing their computing capabilities. Hence, incentive mechanisms are needed to deter users from free-riding. In this paper, we first model the selfish behavior of the users supplying resources and aiming to maximize their own benefits, and compute the performance of the resulting non-cooperative equilibrium, which is highly inefficient. We then augment the existing job allocation schemes currently implemented in social cloud systems with a novel class of incentive mechanisms based on reputation-based pricing and collective punishment schemes that compel suppliers to change their selfish strategies in a manner that improves the efficiency of the system. We study the cloud system operator's problem of jointly optimizing the incentive mechanism and the job allocation scheme in order to find an optimal social cloud protocol which eliminates the free-riding behavior of suppliers while maximizing the social welfare of the system. We rigorously prove that, using only simple designs for both the incentive mechanism and the job allocation scheme, the resulting protocol provides significant improvements in terms of the social welfare compared to existing social cloud systems.

This paper studies the interaction between a designer and a group of strategic and self-interested users who possess information the designer does not have. Because the users are strategic and self-interested, they will act to their own advantage, which will often be different from the interest of the designer, even if the latter is benevolent and seeks to maximize (some measure of) social welfare. In the settings we consider, the designer and the users can communicate (perhaps with noise), the designer can observe the actions of the users (perhaps with error) and the designer can commit to (plans of) actions - interventions - of its own. The designer's problem is to construct and implement a mechanism that provides incentives for the users to communicate and act in such a way as to further the interest of the designer - despite the fact that they are strategic and self-interested and possess private information. To address the designer's problem we propose a general and flexible framework that applies to many scenarios. To illustrate the usefulness of this framework, we discuss some simple examples, leaving further applications to other papers. In an important class of environments, we find conditions under which the designer can obtain its benchmark optimum - the utility that could be obtained if it had all information and could command the actions of the users - and conditions under which it cannot. More broadly we are able to characterize the solution to the designer's problem, even when it does not yield the benchmark optimum. Because the optimal mechanism may be difficult to construct and implement, we also propose a simpler and more readily implemented mechanism that, while falling short of the optimum, still yields the designer a "good" result.

This paper proposes a novel framework for incentivizing self-interested transceivers operating in autonomic wireless networks to provide relaying services to other transceivers in exchange for tokens. Tokens represent a simple internal currency which can be used by the transceivers in a network to exchange services. Our emphasis in this paper is on developing optimal designs for the token system, which maximize the system efficiency, i.e. the probability that the relay transmission will be executed by transceivers whenever they are requested to provide such services. Particularly, we prove that the efficiency of the relay network heavily depends on issuing the proper amount of tokens rather than an arbitrary amount. First, we study the transceivers' optimal strategies (i.e. the strategies that maximize the transceivers' own utilities) using the formalism of repeated games. We prove that these strategies exhibit a simple threshold structure. We also prove that the threshold is unique given transmission costs. Second, we determine the optimal token amount which needs to be introduced in the relay system to maximize the overall relay network efficiency. This amount needs to be neither too small (since a too small amount leads to a small relaying service request probability) nor too large (since a too large amount leads to a small relaying service provision probability) and depends on the threshold strategy that the self-interested transceivers adopt. We subsequently develop an efficient algorithm which is able to determine, depending on the network characteristics, the threshold to be implemented by the optimal strategies and the optimal token amount. Finally, simulation results show the effectiveness of our token system design in providing incentives for cooperation among self-interested relays in autonomic wireless relay networks.

Electric vehicles (EVs) will play an important role in the future smart grid because of their capabilities of storing electrical energy in their batteries during off-peak hours and supplying the stored energy to the power grid during peak hours. In this paper, we consider a power system with an aggregator and multiple customers with EVs and propose novel electricity load scheduling algorithms which, unlike previous works, jointly consider the load scheduling for appliances and the energy trading using EVs. Specifically, we allow customers to determine how much energy to purchase from or to sell to the aggregator while taking into consideration the load demands of their residential appliances and the associated electricity bill. We propose two different approaches: a collaborative and a non-collaborative approach. In the collaborative approach, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare of the power system. In the non-collaborative approach, we model the energy scheduling problem as a non-cooperative game among self-interested customers, where each customer determines its own load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers in the non-collaborative approach, we propose a tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. In both approaches, we also consider the uncertainty in the load demands, with which customers' actual energy consumption may vary from the scheduled energy consumption. To study the impact of the uncertainty, we use the worst-case-uncertainty approach and develop distributed load scheduling algorithms that provide the guaranteed minimum performances in uncertain environments. Subsequently, we show when energy trading leads to an increase in the social welfare and we determine what are the customers' incentives to participate in the energy trading in various usage scenarios including practical environments with uncertain load demands.

Real-time stream mining such as surveillance and personal health monitoring, which involves sophisticated mathematical operations, is computation-intensive and prohibitive for mobile devices due to the hardware/computation constraints. To satisfy the growing demand for stream mining in mobile networks, we propose to employ a cloud-based stream mining system in which the mobile devices send via wireless links unclassified media streams to the cloud for classification. We aim at minimizing the classification-energy cost, defined as an affine combination of classification cost and energy consumption at the cloud, subject to an average stream mining delay constraint (which is important in real-time applications). To address the challenge of time-varying wireless channel conditions without a priori information about the channel statistics, we develop an online algorithm in which the cloud operator can dynamically adjust its resource provisioning on the fly and the mobile devices can adapt their transmission rates to the instantaneous channel conditions. It is proved that, at the expense of increasing the average stream mining delay, the online algorithm achieves a classification-energy cost that can be pushed arbitrarily close to the minimum cost achieved by the optimal offline algorithm. Extensive simulations are conducted to validate the analysis.

This paper presents the first study of the endogenous formation of networks by strategic, self-interested agents who benefit from producing and disseminating information. This work departs from previous works on network formation (especially in the economics literature) which assume that agents benefit only by acquiring information produced by other agents. The strategic production and dissemination of information have striking consequences. We show first that the network structure that emerges (in equilibrium) typically displays a core-periphery structure, with the few agents at the core playing the role of eeconnectorsee, creating and maintaining links to the agents at the periphery. We then determine conditions under which the networks that emerge are minimally connected and have short network diameters (properties that are important for efficiency). Finally, we show that the number of agents who produce information and the total amount of information produced in the network grow at the same rate as the agent population; this is in stark contrast to the "law of the few" that had been established in previous works which do not consider information dissemination.

The increase of Internet application requirements, such as throughput and delay, has spurred the need for transport protocols with flexible transmission control. Current TCP congestion control adopts an Additive Increase Multiplicative Decrease (AIMD) algorithm that linearly increases or exponentially decreases the congestion window based on transmission acknowledgments. In this paper, we propose an AIMD-like media-aware congestion control that determines the optimal congestion window updating policy for multimedia transmission. The media-aware congestion control problem is formulated as a Partially Observable Markov Decision Process (POMDP), which maximizes the long-term expected quality of the received multimedia application. The solution of this POMDP problem gives a policy adapted to multimedia applications' characteristics (i.e., distortion impacts and delay deadlines of multimedia packets). Note that to obtain the optimal congestion policy, the sender requires the complete statistical knowledge of both multimedia traffic and the network environment, which may not be available in practice. Hence, an online reinforcement learning in the POMDP-based solution provides a powerful tool to accurately estimate the environment and to adapt the source to network variations on the fly. Simulation results show that the proposed online learning approach can significantly improve the received video quality while maintaining the responsiveness and TCP-friendliness of the congestion control in various network scenarios.

Online television (TV) market has been expanding rapidly over the last few years and provided TV studios with a cost-effective and reliable channel for the delivery of high-quality TV content. To maximize profit by setting up an online TV content platform, two major challenges are faced by the platform owner: what is the optimal investment (e.g., how many hosting servers, bandwidth acquisition) and how to price TV content producers who utilize the platform as a channel to distribute their content. To address these two challenges, we first derive the optimal pricing policy based on the widely-adopted “pay-per-usage” model, and then formalize and solve the optimal investment decision problem. Rationality of self-interested TV content producers and audiences is also taken into account. Specifically, we first use a model with a representative content viewer to determine how many times a TV content with a certain quality is watched. Then, by modeling the content providers as self-interested agents making independent production decisions, we show that for any price charged by the platform, there always exists a unique equilibrium in the content production stage, which makes it possible for the platform owner to maximize its profit without uncertainties because of the unique outcome in the content producers' decision stage. Finally, we develop an algorithm to derive the optimal price and then formalize the investment decision problem to maximize the platform's profit.

In this work, we propose a method to reduce the impact of process variations by adapting the application's algorithm at the software layer. We introduce the concept of hardware signatures as the measured post manufacturing hardware characteristics that can be used to drive software adaptation across different die. Using H.264 encoding as an example, we demonstrate significant yield improvements (as much as 30% points at 0% hardware overdesign), a reduction in overdesign (by as much as 8% points at 80% yield) as well as application quality improvements (about 2.0 dB increase in average peak-signal-to-noise ratio at 70% yield). Further, we investigate implications of limited information exchange (i.e., signature quantization) on yield and quality. We conclude that hardware-signature-based application adaptation is an easy and inexpensive (to implement), better informed (by actual application requirements) and effective way to manage yield-cost-quality tradeoffs in application-implementation design flows.

Nowadays, multimedia applications and specifically streaming systems over wireless networks use the TCP transport protocol. Indeed, TCP can deal with practical issues such as firewalls and also deploys built-in retransmissions and congestion control mechanisms. We propose in this paper a Quality-centric Mean Opinion Score (MOS) based congestion control that determines an optimal congestion window updating policy for multimedia transmission. Unlike the standard congestion control algorithms, our approach defines a new Additive Increase Multiplicative Decrease (AIMD) algorithm given the multimedia application and the transmission characteristics. In order to get the optimal congestion policy in practice, the sender requires complete statistical knowledge of both multimedia traffic and the network environment, which may not be available in wireless systems. Hence, we propose in this paper, a Partially Observable Markov Decision Process (POMDP) framework in order to determine an optimal congestion control policy which maximizes the long term expected Quality of Experience (QoE) of the receiver. Moreover, the computation of an optimal policy is usually time/process consuming and as wireless devices are capacity-limited, we consider optimal solutions based on temporal difference (TD-λ) online learning algorithms. Finally, we do some practical experiments of our algorithm on a Microsoft Lync testbed with unidirectional and bidirectional communications over a wireless network. We observe that for both scenarios, our algorithm improves significantly the QoE compared to standard AIMD congestion control mechanism.

In this paper, we propose a quality-centric congestion control for multimedia streaming over wired IP networks, which we refer to as media-TCP-friendly congestion control (MTCC). Our solution adapts the sending rate to both the network condition and the application characteristics by explicitly considering the distortion impacts, delay deadlines, and interdependencies of different video packet classes. The media-aware solution is able to provide differential services for transmitting various packet classes and thereby, further improves the multimedia streaming quality compared to the conventional network-aware congestion control. We use finite-horizon Markov decision process (FHMDP) to determine the optimal congestion control policy that maximizes the long-term multimedia quality, while adhering to the horizon-K TCP-friendliness constraint, which ensures long-term fairness with existing TCP applications. Moreover, the proposed MTCC is able to achieve quality-based fairness among multimedia users. We derive sufficient conditions for multiple multimedia users to achieve quality-based fairness using MTCC congestion control. Note that the proposed solution only modifies the adaptation mechanism of the TCP congestion window size at the sender, without changing the design at the receiver side (i.e., each current TCP receiver can correctly receive and process MTCC streams). Our simulation results show that MTCC achieves more than 3 dB improvement in terms of PSNR over the conventional TCP congestion control approaches, with the largest improvements observed for real-time streaming applications requiring stringent playback delays.

In this paper, we focus on the users' aggregate data demand dynamics in a wireless communications market served by a monopolistic wireless service provider (WSP). Based on the equilibrium data demand, we optimize the WSP's data plans and long-term network capacity decisions to maximize its profit. First, by considering a market where only one data plan is offered, we show that there exists a unique equilibrium in the data demand dynamics regardless of the data plans, and that the convergence of data demand dynamics is subject to the network congestion cost, which is closely related to the WSP's long-term capacity decision. A sufficient condition on the network congestion cost indicates that the WSP needs to provide a sufficiently large network capacity to guarantee the convergence of data demand dynamics. We also propose a heuristic algorithm that progressively optimizes the WSP's data plan to maximize its equilibrium revenue. Next, we turn to a market where two different data plans are offered. It is shown that the existence of a unique equilibrium data demand depends on the data plans, and the convergence of data demand dynamics is still subject to the network congestion cost (and hence, the WSP's network capacity, too). We formalize the problem of optimizing the WSP's data plans and network capacities to maximize its profit. Finally, we discuss the scenario in which the data plans are offered by two competing WSPs and conduct extensive simulations to validate our analysis.

Delay-sensitive video transmission over IEEE 802.11 wireless local area networks (WLANs) is analyzed in a cross-layer optimization framework. The effect of delay constraint on the quality of received packets is studied by analyzing “expired-time packet discard rate”. Three analytical models are examined and it is shown that M/M/1 model is quite an adequate model for analyzing delay-limited applications such as live video transmission over WLAN. The optimal MAC retry limit corresponding to the minimum “total packet loss rate” is derived by exploiting both mathematical analysis and NS-2 simulations. We have shown that there is an interaction between "packet overflow drop" and "expired-time packet discard" processes in the queue. Subsequently, by introducing the concept of virtual buffer size, we will obtain the optimal buffer size in order to avoid "packet overflow drop". We finally introduced a simple and yet effective real-time algorithm for retry-limit adaptation over IEEE 802.11 MAC in order to maintain a loss protection for delay-critical video traffic transmission, and showed that the average link-layer throughput can be improved by using our adaptive scheme.

Some users of a communications network may have more information about traffic on the network than others do-and this fact may be secret. Such secret information would allow the possessor to tailor its own traffic to the traffic of others; this would help the secret information possessor or informed user and (might) harm other uninformed users. To quantitatively study the impact of secret information, we formulate a flow control game with incomplete information where users choose their flows in order to maximize their (expected) utilities given the distribution of the actions of others. In this environment, the natural baseline notion is Bayesian Nash Equilibrium (BNE); we establish the existence of BNE. Next, we assume that there is a user who knows the realized congestion created by other users, but that the presence of this informed user is not known by other uninformed users; thus, the informed user has secret information. For this environment, we define a new equilibrium concept: the Bayesian Nash Equilibrium with Secret Information (BNE-SI) and establish its existence. We establish rigorous estimates for the benefit (to the informed user) and harm (to the uninformed users) that result from secret information; both the benefit and the harm become smaller for large networks. Interestingly, simulations demonstrate that secret information may in fact benefit all users. Secret information may also harm uninformed users in particular scenarios. This analysis can be used as a starting point for securing communications networks, both from the network manager and the user's perspectives.

In this paper, we consider a wireless amplify-and-forward relay network with one relay node and multiple source-destination pairs/users and propose a pricing framework that enables the relay to set prices to maximize either its revenue or any desirable system utility. Specifically, depending on the quality of the received signals, the relay sets prices and correspondingly charges the users utilizing its resources for their transmissions. The price is determined in such a way that the relay's revenue or system utility is maximized. Given the specified price, the users competitively employ the relay node to forward their signals. We model each user as a rational player, which aims at maximizing its own net utility through power allocation, and analyze the competition among the users within the framework of noncooperative game theory. It is shown that, in the game played by the users, there always exists a unique pure Nash equilibrium point that can be achieved through distributed iterations. Next, subject to the availability of complete information about the users at the relay, we propose a low-complexity uniform pricing algorithm and an optimal differentiated pricing algorithm, in which the relay either charges the users at a suboptimal uniform price or charges different users at different prices. We also show that, by applying the differentiated pricing algorithm that enforces the users to transmit at certain power levels, any system utility can be maximized. Extensive simulations are conducted to quantify the performance of the proposed methods.

We consider wireless communication networks where network users are subject to critical events such as emergencies and crises. If a critical event occurs to a user, the user needs to send critical traffic as early as possible. However, most existing medium access control (MAC) protocols are not adequate to meet the urgent need for data transmission by users with critical traffic. In this paper, we devise a class of distributed MAC protocols that achieve coordination using the finite-length memory of users containing their own observations and traffic types. We formulate a protocol design problem and find optimal protocols that solve the problem. We show that the proposed protocols enable a user with critical traffic to transmit its critical traffic without interruption from other users after a short delay while allowing users to share the channel efficiently when there is no critical traffic. Moreover, the proposed protocols require short memory and can be implemented without explicit message passing., 24 pages, 7 figures, 1 table

Multimedia stream mining applications require the identification of several different attributes in data content, and hence rely on a set of cascaded statistical classifiers to filter and process the data dynamically. In this paper, we introduce a novel methodology for configuring such cascaded classifier topologies, specifically binary classifier trees, in resource-constrained, distributed stream mining systems. Instead of traditional load shedding, our approach configures classifiers with optimized operating points after jointly considering the misclassification cost of each end-to-end class of interest in the tree, the resource constraints for every classifier, and the confidence level of each data object that is classified. The proposed approach allows for both intelligent load shedding as well as data replication based on available resources dynamically. We evaluate the algorithm on a sports video concept detection application and identify huge cost savings over load shedding alone. Additionally, we propose several distributed algorithms that enable each classifier in the tree to reconfigure itself based on local information exchange. We analyze the associated tradeoffs between convergence time, information overhead, and the cost efficiency of results achieved by each classifier for each of these algorithms.

Multi-hop wireless networks can provide flexible network infrastructures at a low cost. However, most existing wireless networking solutions are designed for delay-insensitive applications, thereby resulting in poor performance when handling delay-sensitive applications. Traditionally, network design problems are formulated as static optimizations, by assuming the network characteristics remain static. However, these solutions are not optimal when the environments are dynamic. Recently, several research works apply machine learning to maximize the performance of multi-hop wireless networks in dynamic environments, but they either only focus on determining policies at the network layer, without considering the lower-layers' actions, or use centralized learning approaches, which are inefficient for delay-sensitive applications, due to the large delay when propagating messages throughout the network. We propose in this paper a new solution that enables the nodes to autonomously determine their routing and transmission power to maximize the network utility, in a dynamic environment. We formulate the problem as a Markov Decision Process, and propose a distributed computation of the optimal policy. Moreover, we use reinforcement-learning to find the optimized policy when the dynamics are unknown. We explicitly consider the impact of the information overhead on the network performance, and propose several novel algorithms to reduce the information overhead.

In this paper, we formulate the collaborative multi-user wireless video transmission problem as a multi-user Markov decision process (MUMDP) by explicitly considering the users' heterogeneous video traffic characteristics, time-varying network conditions and the resulting dynamic coupling between the wireless users. These environment dynamics are often ignored in existing multi-user video transmission solutions. To comply with the decentralized nature of wireless networks, we propose to decompose the MUMDP into local MDPs using Lagrangian relaxation. Unlike in conventional multi-user video transmission solutions stemming from the network utility maximization framework, the proposed decomposition enables each wireless user to individually solve its own dynamic cross-layer optimization (i.e. the local MDP) and the network coordinator to update the Lagrangian multipliers (i.e. resource prices) based on not only current, but also future resource needs of all users, such that the long-term video quality of all users is maximized. However, solving the MUMDP requires statistical knowledge of the experienced environment dynamics, which is often unavailable before transmission time. To overcome this obstacle, we then propose a novel online learning algorithm, which allows the wireless users to update their policies in multiple states during one time slot. This is different from conventional learning solutions, which often update one state per time slot. The proposed learning algorithm can significantly improve the learning performance, thereby dramatically improving the video quality experienced by the wireless users over time. Our simulation results demonstrate the efficiency of the proposed MUMDP framework as compared to conventional multi-user video transmission solutions., Comment: 36 pages, 13 figures

In this paper, we propose a general cross-layer optimization framework in which we explicitly consider both the heterogeneous and dynamically changing characteristics of delay-sensitive applications and the underlying time-varying network conditions. We consider both the independently decodable data units (DUs, e.g. packets) and the interdependent DUs whose dependencies are captured by a directed acyclic graph (DAG). We first formulate the cross-layer design as a non-linear constrained optimization problem by assuming complete knowledge of the application characteristics and the underlying network conditions. The constrained cross-layer optimization is decomposed into several cross-layer optimization subproblems for each DU and two master problems. The proposed decomposition method determines the necessary message exchanges between layers for achieving the optimal cross-layer solution. However, the attributes (e.g. distortion impact, delay deadline etc) of future DUs as well as the network conditions are often unknown in the considered real-time applications. The impact of current cross-layer actions on the future DUs can be characterized by a state-value function in the Markov decision process (MDP) framework. Based on the dynamic programming solution to the MDP, we develop a low-complexity cross-layer optimization algorithm using online learning for each DU transmission. This online algorithm can be implemented in real-time in order to cope with unknown source characteristics, network dynamics and resource constraints. Our numerical results demonstrate the efficiency of the proposed online algorithm., Comment: 30 pages, 10 figures

In this paper, we consider the resource reciprocation among self-interested peers in peer-to-peer (P2P) networks, which is modeled as a stochastic game. Peers play the game by determining their optimal strategies for resource distributions using a Markov decision process (MDP) framework. The optimal strategies enable the peers to maximize their long-term utility. Unlike in conventional MDP frameworks, we consider heterogeneous peers that have different and limited ability to characterize their resource reciprocation with other peers. This is due to the large complexity requirements associated with their decision making processes. We analytically investigate these tradeoffs and show how to determine the optimal number of state descriptions, which maximizes each peer's average cumulative download rates given a limited time for computing the optimal strategies. We also investigate how the resource reciprocation evolves over time as peers adapt their reciprocation strategies by changing the number of state descriptions. Then, we study how resulting download rates affect their performance as well as that of the other peers with which they interact. Our simulation results quantify the tradeoffs between the number of state descriptions and the resulting utility. We also show that evolving resource reciprocation can improve the performance of peers which are simultaneously refining their state descriptions.

Recent research in wireless multimedia streaming has focused on optimizing the multimedia quality in isolation, at each station. However, the cross-layer transmission strategy deployed at one station impacts and is impacted by the other stations, as the wireless network resource is shared among all competing users. Hence, efficient and fair resource management for autonomous wireless multimedia users becomes very important. We consider quality-based fairness schemes based on axiomatic bargaining theory, which can ensure that the autonomous multimedia stations incur the same drop in multimedia quality as compared to a maximum achievable quality for each wireless station. Implementing this quality-based fairness solution in the time-varying channel condition requires high-computational complexity and communication overheads. Hence, we develop solutions that significantly reduce the computational complexity and communication overheads. Our simulations show that the proposed game-theoretic resource management can indeed guarantee desired utility-fair allocations when wireless stations deploy different cross-layer strategies.

In this paper, we assume that the network resources are managed by several brokers, which are endowed with resources by a (remote) central resource manager according to several predetermined policies. Our focus is on autonomous multimedia users. We propose a novel resource management scheme, where resource brokers choose well-suited axiomatic bargaining solutions to divide their allocated resources among the users associated with them. These resource division solutions enable resource brokers to provide strict minimum video quality guarantees according to the (varying) number of multimedia users associated with them. Finally, we show that the proposed solution enables us to model the problem of selecting resource brokers by multimedia users as an unweighted congestion game, thereby ensuring convergence to a stationary distribution of users across resource brokers. We investigate the number of required users' switches to reach the stationary distribution, and quantify the fairness of the stationary distribution by introducing a novel quality fairness comparison metric for the users.

In this letter, we consider peer-to-peer (P2P) networks, where multiple peers are interested in sharing their content. In the considered P2P system, autonomous and self-interested peers use a Markov Decision Process (MDP) framework to determine their upload bandwidth allocations, which maximize their individual utilities. This framework enables the peers to make foresighted decisions on their bandwidth allocations, by considering the future impact of their decisions. In this letter, we focus on the impact of the peers’ bounded rationality on their resource reciprocation strategies and ultimately, on their achievable utilities. Specifically, we consider peers who have only a limited ability to model the other peers' strategies for resource reciprocation, and study how this impacts their own decisions.

Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time-feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding.

This paper studies the value of learning for cognitive transceivers in dynamic wireless networks. We quantify the utility improvement that can be obtained by a wideband user that learns the stationary usage pattern of the spectrum occupied by narrowband users and, based on this learned information, adapts its transmission. Specifically, we investigate the basic tradeoff between the learning duration and the achievable performance in stationary environments. We apply optimization and large-deviation theory to analytically derive an upper bound of the minimum required learning duration, given the user's tolerable performance loss and outage probability. Furthermore, since learning techniques require the information feedback of the spectrum usage pattern between the transceivers, we investigate how a cognitive user can further improve its performance by taking into account its feedback delay. The impact of inaccurate delay estimation on the achievable performance is also quantified.

Designing efficient and fair solutions for dividing the network resources in a distributed manner among self-interested multimedia users is recently becoming an important research topic because heterogeneous and high bandwidth multimedia applications (users), having different quality-of-service requirements, are sharing the same network. Suitable resource negotiation solutions need to explicitly consider the amount of information exchanged among the users and the computational complexity incurred by the users. In this paper, we propose decentralized solutions for resource negotiation, where multiple autonomous users self-organize into a coalition which shares the same network resources and negotiate the division of these resources by exchanging information about their requirements. We then discuss various resource sharing strategies that the users can deploy based on their exchanged information. Several of these strategies are designed to explicitly consider the utility (i.e., video quality) impact of multimedia applications. In order to quantify the utility benefit derived by exchanging different information, we define a new metric, which we refer to as the value of information. We quantify through simulations the improvements that can be achieved when various information is exchanged between users, and discuss the required complexity at the user side involved in implementing the various resource negotiation strategies.

Cross-layer optimization solutions have been proposed in recent years to improve the performance of wireless users that operate in a time-varying, error-prone network environment. However, these solutions often rely on centralized cross-layer optimization solutions that violate the layered network architecture of the protocol stack by requiring layers to provide access to their internal protocol parameters to other layers. This paper presents a new systematic framework for cross-layer optimization, which allows each layer to make autonomous decisions to maximize the wireless user's utility by optimally determining what information should be exchanged among layers. Hence, this cross-layer framework preserves the current layered network architecture. Since the user interacts with the wireless environment at various layers of the protocol stack, the cross-layer optimization problem is solved in a layered fashion such that each layer adapts its own protocol parameters and exchanges information (messages) with other layers that cooperatively maximize the performance of the wireless user. Based on the proposed layered framework, we also design a message-exchange mechanism that determines the optimal cross-layer transmission strategies, given the user's experienced environment dynamics.

In this paper, we study the problem of dynamic resource management for delay-sensitive users over wireless networks. We focus on a decentralized setting, where autonomous users make self-interested decisions to maximize their utility functions as evaluated based on information feedback. In this paper, two types of information feedback are discussed. One is the private information feedback between a transmitter-receiver pair. The other is the public information feedback among users (i.e., different transmitter-receiver pairs). Due to the informationally decentralized nature of the wireless network, a user cannot have complete information about the transmission actions of its interfering neighbors. However, the user can implicitly or explicitly model the transmission strategies of its major interference sources based on the information feedback. In this paper, we provide an interactive learning framework for distributed power control of delay-sensitive users over multicarrier wireless networks. Specifically, the user can adopt corresponding interactive learning schemes to explicitly model the other users' strategies if public information feedback is available or to implicitly model the impact of other users' actions on its utility if only private information is available. Based on these models, the user creates beliefs and is able to strategically adapt its decisions to maximize its utility. We determine the performance upper bounds for the user's utility when learning from private or public information feedback and investigate the cost-performance tradeoffs resulting from the information feedback gathered with different frequencies and from various users. The simulation results show that the proposed adaptive interactive learning approach significantly improves the energy efficiency of delay-sensitive users compared with schemes that perform myopic best response.

In this paper, we model the various users in a wireless network (e.g., cognitive radio network) as a collection of selfish autonomous agents that strategically interact to acquire dynamically available spectrum opportunities. Our main focus is on developing solutions for wireless users to successfully compete with each other for the limited and time-varying spectrum opportunities, given experienced dynamics in the wireless network. To analyze the interactions among users given the environment disturbance, we propose a stochastic game framework for modeling how the competition among users for spectrum opportunities evolves over time. At each stage of the stochastic game, a central spectrum moderator (CSM) auctions the available resources, and the users strategically bid for the required resources. The joint bid actions affect the resource allocation and, hence, the rewards and future strategies of all users. Based on the observed resource allocations and corresponding rewards, we propose a best-response learning algorithm that can be deployed by wireless users to improve their bidding policy at each stage. The simulation results show that by deploying the proposed best-response learning algorithm, the wireless users can significantly improve their own bidding strategies and, hence, their performance in terms of both the application quality and the incurred cost for the used resources.

We propose a novel cross layer solution for optimizing the performance of multiple users, which are concurrently transmitting their delay-sensitive video streams over the same shared contention-based wireless LAN. To improve the performance of their applications, they dynamically adapt their medium access control (MAC) strategies (i.e., retry limits). Higher retry limits will benefit the user, but they will also lead to an increased congestion for the other users. Hence, we explicitly consider how the cross layer strategy adopted by one video user will impact the strategies of the competing users. Specifically, we analytically model how the cross layer transmission strategies adopted by each wireless user impact their own incurred distortion and delay but also that of their competing wireless stations. We also propose a distributed, low-complexity, and highly scalable optimization algorithm which can maximize the utility of the delay-sensitive video application. We compare our scheme to existing retry limit-based solutions, and the simulation results show that the proposed solution can outperform these solutions and maximize the users' utilities in a highly congested multiuser wireless environment.

With the current proliferation of high bandwidth and delay-sensitive multimedia applications and services, each wireless user will try to maximize its utility by acquiring as much spectrum resources as possible unless a preemptive mechanism exists in the network. Thus, emerging solutions for dynamic spectrum access in cognitive radio networks will need to adopt market-based approaches in order to effectively regulate the available resources. In this paper, we show how various centralized and decentralized spectrum access markets can be designed based on a stochastic game framework, where wireless users (also referred to as secondary users) can compete over time for the dynamically available transmission opportunities (spectrum ldquoholesrdquo). When operating in such spectrum access ldquomarkets,rdquo wireless users become selfish, autonomous agents that strategically interact in order to acquire the necessary spectrum opportunities. We also show how wireless users can successfully compete with each other for the limited and time-varying spectrum opportunities, given the experienced dynamics in the wireless network, by optimizing both their external actions (e.g., the resource bids, power and channel used for transmission, etc.) and their internal actions (e.g., the modulation schemes, etc.). To determine their optimal actions in an informationally decentralized setting, users will need to learn and model directly or indirectly the other users' responses to their external actions. We studied the outcome of various dynamic interactions among self-interested wireless users possessing different knowledge and determine that the proposed framework can lead to multiuser communication systems that achieve new measures of efficiency, rationality and fairness. Lastly, our illustrative results show that the presented game-theoretic solution for wireless resource management enables users deploying enhanced (ldquosmarterrdquo) learning and communication algorithms and being able to make efficient use of the spectrum resources can derive higher utilities. This presents the designers of wireless devices and systems with important incentives to endow their next-generation products and services with enhanced capabilities to gather information, learn, and strategically compete for resources in the emerging spectrum resource markets made possible by the cognitive radio network technologies.

In this paper, we investigate the problem of multiuser resource management in multihop cognitive radio networks for delay-sensitive applications. Since tolerable delay does not allow propagating global information back and forth throughout the multihop network to a centralized decision maker, the source nodes and relays need to adapt their actions (transmission frequency channel and route selections) in a distributed manner, based on local network information. We propose a distributed resource-management algorithm that allows network nodes to exchange information and that explicitly considers the delays and cost of exchanging the network information over multihop cognitive radio networks. In this paper, the term ldquocognitiverdquo refers to both the capability of the network nodes to achieve large spectral efficiencies by dynamically exploiting available frequency channels and their ability to learn the ldquoenvironmentrdquo (the actions of interfering nodes) based on the designed information exchange. Note that the node competition is due to the mutual interference of neighboring nodes using the same frequency channel. Based on this, we adopt a multiagent-learning approach, i.e., adaptive fictitious play, which uses the available interference information. We also discuss the tradeoff between the cost of the required information exchange and the learning efficiency. The results show that our distributed resource-management approach improves the peak signal-to-noise ratio (PSNR) of multiple video streams by more than 3 dB, as opposed to the state-of-the-art dynamic frequency channel/route selection approaches without learning capability, when the network resources are limited.

In this paper, we study the system-level computational resource allocation problem among multiple multimedia tasks. We consider the multimedia tasks to be autonomous, i.e., they are selfish and behave strategically. We propose a resource allocation framework based on mechanism design to prevent the tasks from behaving strategically and manipulating the available system resources. We apply two mechanisms in the framework and assess their advantages over proportional-share resource allocation algorithms, which are often used in multimedia systems. We show in the simulations that the incorporation of mechanism design for system resource allocation is a promising solution that achieves efficient, fair and robust allocation against manipulation from strategic applications.

In this paper, we consider a peer-to-peer (P2P) network, where multimedia streams are broadcast by matched peers based on their resource reciprocation profiles. We propose a new framework where each peer creates a coalition of matched peers with which it can exchange resources in order to improve its utility. The utility is determined based on explicit consideration of the peer's multimedia attributes and the quality derived by the peers' reciprocation behavior. We adopt the proportional bargaining solution to negotiate the upload bandwidth among the matched peers. Proportional bargaining allows to determine each peer's optimal (in a Pareto optimal sense) upload rates in a coalition in terms of its utility impact. The impact of an incoming peer on the coalition value, which represents the collective utility achieved by the peers in a coalition, is assessed by explicitly investigating the coalition value improvement. Finally, our results show that the proposed coalition-based resource reciprocation can improve the resource allocation/scheduling algorithms deployed in existing P2P systems such as BitTorrent and CoolStreaming. We also discuss how the proposed resource reciprocation approach can be implemented in other multimedia broadcasting applications.

We study the problem of optimal resource allocation for multi-user multiaccess wireless video communication from an information-theoretic point of view. We derive the optimal resource allocation policies by directly maximizing at the application layer the weighted sum of video qualities of all users, subject to information-theoretic multiaccess capacity region constraints in the MAC-PHY layers. We solve this problem for three multiaccess capacity regions: (1) non-fading channel, (2) fading channel with a given power control policy, and (3) fading channel with dynamic power control policies. The optimal resource allocation policy is referred as largest quality improvement highest possible rate (LQIHPR). We propose simple greedy algorithms to implement this policy. Since the capacity region is the fundamental characterization of achievable rates, the solutions developed in this paper provide the operational upper bound of achievable video quality in a multiaccess fading channel.

In this paper, we propose a dynamic channel-selection solution for autonomous wireless users transmitting delay-sensitive multimedia applications over cognitive radio networks. Unlike prior works that seldom consider the requirement of the application layer, our solution explicitly considers various rate requirements and delay deadlines of heterogeneous multimedia users. Note that the users usually possess private utility functions, application requirements, and distinct channel conditions in different frequency channels. To efficiently manage available spectrum resources in a decentralized manner, information exchange among users is necessary. Hence, we propose a novel priority virtual queue interface that determines the required information exchanges and evaluates the expected delays experienced by various priority traffics. Such expected delays are important for multimedia users due to their delay-sensitivity nature. Based on the exchanged information, the interface evaluates the expected delays using priority queuing analysis that considers the wireless environment, traffic characteristics, and the competing users' behaviors in the same frequency channel. We propose a dynamic strategy learning (DSL) algorithm deployed at each user that exploits the expected delay and dynamically adapts the channel selection strategies to maximize the user's utility function. We simulate multiple video users sharing the cognitive radio network and show that our proposed solution significantly reduces the packet loss rate and outperforms the conventional single-channel dynamic resource allocation by almost 2 dB in terms of video quality.

Stream mining applications require the identification of several different attributes in data content and hence rely on a distributed set of cascaded statistical classifiers to filter and process the data dynamically. In this letter, we introduce a novel methodology for configuring cascaded classifier topologies, specifically binary classifier trees, with optimized operating points after jointly considering the misclassification cost of each end-to-end class of interest in the tree, the resource constraints for every classifier, and the confidence level of each data object that is classified. By configuring multiple operating points per classifier, we enable not only intelligent load shedding when resources are scarce but also intelligent replication of low confidence data across multiple edges when excess resources are available. Using a classifier tree constructed from support vector machine-based sports image classifiers, we verify huge cost savings and discuss how different classifier placements and costs can influence the gains obtained by various algorithms.

Available resources in peer-to-peer (P2P) systems depend strongly on resource contributions made by individual peers. Empirical data shows that in the absence of incentives, a majority of the participating peers do not contribute resources. Modeling interactions between individual peers is often difficult as the number of peers in the system can be very large, and the relationships among them can be very complex. In this paper, we propose a new solution for P2P systems, where peers upload and download content to and from the contributing peers based on agreed-upon/determined sharing rates. We propose a P2P solution that deters free-riders by imposing constraints on participating peers; specifically, a peer is allowed access to new content only as long as its own content contribution exceeds an adaptively set threshold. The constraints are enforced either by a central authority (e.g., a tracker) or by a decentralized coalition of peers in a swarm, social network, etc. We derive optimal upload policies for the peers given their estimated future download requirements and their previous contribution (credit) to the other peers. Our results show considerable improvement in the cost-benefit tradeoff for peers that deploy such an optimal policy as compared to heuristic upload policies. We also propose mechanisms based on which the coalition of peers can provide incentives or penalties to participating peers to adjust their policies such that the availability of content and/or number of peers contributing content is maximized.

Networks of classifiers are capturing the attention of system and algorithmic researchers because they offer improved accuracy over single model classifiers, can be distributed over a network of servers for improved scalability, and can be adapted to available system resources. In this paper, we develop algorithms to optimally configure networks (chains) of such classifiers given system processing resource constraints. We first formally define a global performance metric for classifier chains by trading off the end-to-end probabilities of detection and false alarm. We then design centralized and distributed algorithms to provide efficient and fair resource allocation among several classifier chains competing for system resources. We use the Nash bargaining solution from game theory to ensure this. We also extend our algorithms to consider arbitrary topologies of classifier chains (with shared classifiers among competing chains). We present results for both simulated and state-of-the-art classifier chains for speaker verification operating on real telephony data, discuss the convergence of our algorithms to the optimal solution, and present interesting directions for future research.

Multihop networks provide a flexible infrastructure that is based on a mixture of existing access points and stations interconnected via wireless links. These networks present some unique challenges for video streaming applications due to the inherent infrastructure unreliability. In this paper, we address the problem of robust video streaming in multihop networks by relying on delay- constrained and distortion-aware scheduling, path diversity, and retransmission of important video packets over multiple links to maximize the received video quality at the destination node. To provide an analytical study of this streaming problem, we focus on an elementary multihop network topology that enables path diversity, which we term "elementary cell." Our analysis is considering several cross-layer parameters at the physical and medium access control (MAC) layers, as well as application-layer parameters such as the expected distortion reduction of each video packet and the packet scheduling via an overlay network infrastructure. In addition, we study the optimal deployment of path diversity in order to cope with link failures. The analysis is validated in each case by simulation results with the elementary cell topology, as well as with a larger multihop network topology. Based on the derived results, we are able to establish the benefits of using path diversity in video streaming over multihop networks, as well as to identify the cases where path diversity does not lead to performance improvements.

State-of-the-art vehicles are now being equipped with multiple video channels for video-data transmission from multiple surveillance cameras mounted on the automobile, navigation videos reporting the traffic conditions on the planned route, as well as entertainment-multimedia streaming for passengers watching on rear-seat monitors. Wireless LANs provide a low-cost and flexible infrastructure for these emerging in-vehicle multimedia services aimed at the driver's and passengers' safety, convenience, and entertainment. To enable the successful simultaneous deployment of such applications over in-vehicle wireless networks, we propose delay-sensitive streaming and packet-scheduling algorithms that enable simple, flexible, and efficient adaptation of the video bitstreams to the instantaneously changing video source and wireless-channel characteristics while complying with the a priori negotiated quality-of-service (QoS) parameters for that video service. Our focus is on real-time low-cost solutions for multimedia transmission over in-vehicle wireless networks that are derived based on existing protocols defined by QoS-enabled networks, such as the IEEE 802.11e standard. In addition, the aim of this paper is to couple the proposed solutions with a novel multitrack-hinting method that is proposed as an extension of conventional MP4 hint tracks in order to provide real-time adaptation of multimedia streams to multiple quality levels for different in-vehicle applications, depending on their importance and delay constraints. First, the scheduling constraints for these simultaneous wireless video-streaming sessions are analytically expressed as a function of the negotiated QoS parameters. This is imperative because a video stream received from an in-vehicle road-surveillance camera will have a different set of delay and quality constraints in comparison to that of traffic monitoring received from remote video cameras located on the planned route. Hence, transmission parameters, such as peak data rate, maximum burst size, minimum transmission delay, maximum error rate, etc., will differ for the various video streams. For this reason, new low-complexity packet-scheduling algorithms that can fulfill diverse QoS streaming conditions are proposed and analyzed. The proposed algorithms produce viable schedules (i.e., strictly QoS-compliant) that jointly consider the delay constraints and the in-vehicle video-receiver-buffer conditions. Hence, these scheduling schemes can completely avoid the underflow or overflow event of the receiving-device buffer while guaranteeing the agreement between the real-time video traffic and the predetermined traffic specification reached during QoS negotiation for various in-vehicle video channels. When combined with multitrack hinting, an integrated flexible system for adaptive multimedia streaming over QoS-enabled in-vehicle wireless networks can be constructed. We demonstrate the viability of the proposed scheduling mechanisms experimentally by using real video traces under multiple quality levels, as derived by the multitrack-hinting design. In addition, simulations under realistic conditions are also performed to validate the ability of the method to satisfy buffer-occupancy constraints.

Various packet scheduling, dynamic routing, error-protection, and channel adaptation strategies have been proposed at different layers of the protocol stack to address multi-user video streaming over multihop wireless networks. However, these cross-layer transmission strategies can be efficiently optimized only if they use accurate information about the network conditions and hence, are able to timely adapt to network changes. Due to the informationally decentralized nature of the multihop wireless network, performing centralized optimization for delay-sensitive video streaming application based on global information about the network status is not practical. Distributed solutions that adapt the transmission strategies based on timely information feedback need to be considered. To acquire this information feedback for cross-layer adaptation, we deploy an overlay infrastructure, which is able to relay the necessary information about the network status and incurred delays across different network ldquohorizonsrdquo (i.e., across a different number of hops in a predetermined period of time). In this paper, we propose a distributed streaming approach that is optimized based on the local information feedback acquired from the various network horizons. We investigate the distributed cross-layer adaptation at each wireless node by considering the advantages resulting from an accurate and frequent network information feedback from larger horizons as well as the drawbacks resulting from an increased transmission overhead. Based on the information feedback, we can estimate the risk that packets from different priority classes will not arrive at their destination before their decoding deadline expires. Subsequently, the various transmission strategies such as packet scheduling, retransmission limit and dynamic routing policies are adapted to jointly consider the estimated risk as well as the impact in terms of distortion of the different priority classes. Our results show that the proposed dynamic routing policy based on timely information feedback outperforms existing state-of-the-art on-demand routing solutions by more than 2 dB in terms of the received video quality.

We consider the problem of multiuser resource allocation for wireless multimedia applications deployed by autonomous and noncollaborative wireless stations (WSTAs). Existing resource allocation solutions for WLANs are not network-aware and do not take into account the selfish behavior of individual WSTAs. Specifically, the selfish WSTAs can manipulate the network by untruthfully representing their private information (i.e., video characteristics, experienced channel conditions, and deployed streaming strategies). This often results in inefficient resource allocations. To overcome this obstacle, we present a pricing mechanism for message exchanges between the WSTAs and the Central Spectrum Moderator (CSM). The messages represent network-aware resource demands and corresponding prices. We prove that the message exchanges reach the Nash equilibrium and that the resulting equilibrium messages generate allocations which are efficient, budget balanced, and satisfy voluntary participation. The simulation results verify that these properties hold when the WSTAs behave strategically. Additionally, we evaluate the impact of initial prices and network congestion level on the convergence rate of message exchanges.

Multiuser multimedia applications such as enterprise streaming, surveillance, and gaming are recently emerging, and they are often deployed over bandwidth-constrained network infrastructures. To ensure the quality of service (QoS) required by the delay-sensitive and bandwidth intensive multimedia data for these applications, efficient resource (bandwidth) management becomes paramount. We propose to deploy the well-known game theoretic concept of bargaining to allocate the bandwidth fairly and optimally among multiple collaborative users. Specifically, we consider two bargaining solutions for our resource management problem: the Nash bargaining solution (NBS) and the Kalai-Smorodinsky bargaining solution (KSBS). We provide interpretations for the two investigated bargaining solutions for multiuser resource allocation: the NBS can be used to maximize the system utility, while the KSBS ensures that all users incur the same utility penalty relative to the maximum achievable utility. The bargaining strategies and solutions are implemented in the network using a resource manager, which explicitly considers the application-specific distortion for the bandwidth allocation. We show that the bargaining solutions exhibit important properties (axioms) that can be used for effective multimedia resource allocation. Moreover, we propose several criteria for determining bargaining powers for these solutions, which enable us to provide additional flexibility in choosing solution by taking into consideration the visual quality impact, the deployed spatiotemporal resolutions, etc. We also determine the complexity of these solutions for our application and quantify the performance of the proposed bargaining-based resource strategies for different scenarios.

This paper presents efficient mechanisms for delay- sensitive transmission of video over IEEE 802.11a/e wireless local area networks (WLANs). Transmitting video over WLANs in real time is very challenging due to the time-varying wireless channel and video content characteristics. This paper provides a comprehensive view of how to adapt the quality of service signaling, IEEE 802.11e parameters and cross-layer design to optimize the video quality at the receiver. We propose an integrated system view of admission control and scheduling for both contention and poll-based access of IEEE 802.11e medium access control (MAC) protocol and outline the merits of each approach for video transmission. We also show the benefits of using a cross-layer optimization by sharing the application, MAC, and physical layer parameters of the open systems interconnection stack to enhance the video quality. We will show through analysis and simulation that controlling the contention-based access in IEEE 802.11e is simple to realize in real products and how different cross-layer strategies used in poll-based access lead to a larger number of stations being simultaneously admitted and/or a higher video quality for the admitted stations. Finally, we introduce a new concept called time fairness, which is critical in enhancing the video performance when different transmitter-receiver pairs deploy different cross-layer strategies.