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Most congestion control schemes are based on user cooperation, i.e., they implicitly assume that users are willing to share their private information and to take actions such that the network operates efficiently. However, a self-interested and strategic user might exploit such schemes to obtain an individual gain at the expenses of the other users, misrepresenting its private information and overusing the resources. We first quantify the inefficiency of the network in the presence of selfish users for two different scenario: in the complete information case - in which the users have no private information - and in the incomplete information case - in which the users have private information. Then, we ask whether the congestion control scheme can be designed to be robust to self-interested strategic users. To reach this objective, we use an intervention scheme. For the complete information scenario we describe a scheme that is able to give the users an incentive to optimally use the resources. For the incomplete information scenario we describe two schemes that provide the users with an incentive to report truthfully and to use the resources efficiently, although not always optimally. Illustrative results show that the considered schemes can considerably improve the efficiency of the network.

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.

We propose a BitTorrent-like protocol based on an online learning (reinforcement learning) mechanism, which can replace the peer selection mechanisms in the regular BitTorrent protocol. We model the peers' interactions in the BitTorrent-like network as a repeated stochastic game, where the strategic behaviors of the peers are explicitly considered. A peer that applies the reinforcement learning (RL)-based mechanism uses the observations on the associated peers' statistical reciprocal behaviors to determine its best responses and estimate the corresponding impact on its expected utility. The policy determines the peer's resource reciprocations such that the peer can maximize its long-term performance. We have implemented the proposed mechanism and incorporated it into an existing BitTorrent client. Our experiments performed on a controlled Planetlab testbed confirm that the proposed protocol 1) promotes fairness and provides incentives to contributed resources, i.e., high capacity peers improve their download completion time by up to 33 percent, 2) improves the system stability and robustness, i.e., reduces the peer selection fluctuations by 57 percent, and (3) discourages free-riding, i.e., peers reduce their uploads to free-riders by 64 percent as compared to the regular BitTorrent protocol.

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.

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.

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.

An increasing number of networked and battery-powered devices require the simultaneous processing of multiple high-complexity dynamic multimedia tasks. Existing resource allocation solutions include both centralized approaches and distributed approaches. Centralized approaches typically have two fundamental limitations. First, multimedia algorithms typically have a large number of operating points and thus requires high implementation complexity to optimally allocate resources across multiple applications, making centralized solutions unsuitable for dynamic environments. Second, autonomous multimedia applications may not be willing to reveal information about their private utility functions, since such information can be exploited by other selfish applications to gain an unfair performance advantage by strategically misusing the system resources. On the other hand, existing decentralized solutions typically solve only resource-constrained utility maximization problems, without regard to other system platform-dependent costs and objectives, and without considering application dynamics. To address these limitations, we present in this paper a decentralized low-complexity resource management solution that can optimize a variety of system performance objectives without requiring applications to provide their utilities. We demonstrate analytically and experimentally that our algorithms converge quickly to their respective optimal solutions and are thus practical in dynamic environments.

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.

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 recent years, cross-layer multimedia system design and optimization has garnered significant attention; however, there exists no rigorous methodology for optimizing two or more system layers (e.g., the application, operating system, and hardware layers) jointly while maintaining a separation among the decision processes, designs, and implementations of each layer. Moreover, existing work often relies on myopic optimizations, which ignore the impact of decisions made at the current time on the system's future performance. In this paper, we propose a novel systematic framework for jointly optimizing the different system layers to improve the performance of one multimedia application. In particular, we model the system as a layered Markov decision process (MDP). The proposed layered MDP framework enables each layer to make autonomous and foresighted decisions, which optimize the system's long-term performance.

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.

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.

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.

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.

We propose to add a new dimension to existing wireless multimedia communications systems by enabling competing stations to proactively engage in the resource management game by adapting their cross-layer transmission strategies. For this, we model wireless stations (WSTAs) as rational and selfish players competing for available wireless resources in a dynamic game. We focus on polling-based wireless LAN (WLAN) networks, where developing an efficient solution for managing the available transmission opportunities is of paramount importance. The resource allocation game is coordinated by a network moderator, which deploys a novel resource management based on the Vickrey-Clarke-Groves (VCG) mechanism to determine a) the amount of time to be allocated to the various users and b) the transmission cost associated to the allocated resources. The transmission cost is referred to in the VCG mechanism as "transfer" and depends not on the used resources, but rather on the inconvenience (in terms of utility impact) that it causes to other WSTAs. The transfer is introduced in order to discourage WSTAs from lying about their resource requirements. Importantly, this proposed dynamic resource management approach for wireless multimedia applications changes the passive way stations are currently adapting their cross-layer strategies by enabling them to selfishly influence the wireless systems dynamics by proactively adapting their packet scheduling strategies, error protection strategies, etc. Hence, each wireless station can play the resource management game by adapting its multimedia transmission strategy depending on the experienced channel conditions, derived video quality, attitude towards risk, willingness to pay for resources and available information about the wireless network. Our simulations show that using the VCG mechanism the WSTAs do not have any incentives to lie about their resource requirements as otherwise they will be severely penalized by a high transfer. We also show that deploying advanced cross-layer strategies for playing the resource management game significantly benefit the WSTAs' received video quality. The willingness-to-pay for resources is introduced to provide WSTAs a tool to gather additional resources whenever they need to transmit an important (part of a) video sequence by agreeing to pay for resources an increased cost. A novel risk-aware scheduling scheme is also proposed that provides WSTAs the ability to dynamically avoid network congestion and hence, reduce their incurred transfer

Peer-to-peer collaboration paradigms fundamentally change the passive way wireless stations currently adapt their transmission strategies to match available resources, by enabling them to proactively influence system dynamics through exchange of information and resources. In this paper, we focus on delay-sensitive multimedia transmission among multiple peers over wireless multi-hop enterprise mesh networks. We propose a distributed and efficient framework for resource exchanges that enables peers to collaboratively distribute available wireless resources among themselves based on their quality of service requirements, the underlying channel conditions, and network topology. The resource exchanges are enabled by the scalable coding of the video content and the design of cross-layer optimization strategies, which allow efficient adaptation to varying channel conditions and available resources. We compare our designed low complexity distributed resource exchange algorithms against an optimal centralized resource management scheme and show how their performance varies with the level of collaboration among the peers. We measure system utility in terms of the multimedia quality and show that collaborative approaches achieve ~50% improvement over non-collaborative approaches. Additionally, our distributed algorithms perform within 10% system utility of a centralized optimal resource management scheme. Finally, we observe 2-5 dB improvement in decoded PSNR for each peer due to the deployed cross-layer strategy

Scalable video coding offers a flexible representation for video adaptation in multiple dimensions comprising spatial detail and temporal resolution, thus providing great benefits for universal media access (UMA) applications. However, currently most of the approaches address the multidimensional adaptation (MDA) problem in an ad hoc manner. One challenging issue affecting the systematic MDA solution is the difficulty in constructing analytical models in theoretical optimization that capture the relations between video utility and MDA operations. In this paper, we propose a general classification-based prediction framework for selecting the preferred MDA operations based on subjective quality evaluation. For this purpose, we first apply domain-specific knowledge or general unsupervised clustering to construct distinct categories within which the videos share similar preferred MDA operations. Thereafter, a machine learning based method is applied where the low level content features extracted from the compressed video streams are employed to train a framework for the problem of joint signal-to-noise ratio (SNR)-temporal adaptation selection based on the motion compensated three-dimensional subband coding (MC-3DSBC) system. We conduct extensive subjective tests involving 31 subjects, 128 video clips, and formal subjective quality metrics. Statistical analysis of the experimental results confirms the excellent accuracy in using domain knowledge and content features to predict the MDA operation.

Real-time streaming of audiovisual content over the Internet is emerging as an important technology area in multimedia communications. Due to the wide variation of available bandwidth over Internet sessions, there is a need for scalable video coding methods and (corresponding) flexible streaming approaches that are capable of adapting to changing network conditions in real time. In this paper, we describe a new scalable video-coding framework that has been adopted recently by the MPEG-4 video standard. This new MPEG-4 video approach, which is known as Fine-Granular-Scalability (FGS), consists of a rich set of video coding tools that support quality (i.e., SNR), temporal, and hybrid temporal-SNR scalabilities. Moreover, one of the desired features of the MPEG-J FGS method is its simplicity and flexibility in supporting unicast and multicast streaming applications over IF.

Information exchange systems differ in many ways, but all share a common vulnerability to selfish behavior and free-riding. In this paper, we build incentives schemes based on social norms. Social norms prescribe a social strategy for the users in the system to follow and deploy reputation schemes to reward or penalize users depending on their behaviors. Because users in these systems often have only limited capability to observe the global system information, e.g. the reputation distribution of the users participating in the system, their beliefs about the reputation distribution are heterogeneous and biased. Such belief heterogeneity causes a positive fraction of users to not follow the social strategy. In such practical scenarios, the standard equilibrium analysis deployed in the economics literature is no longer directly applicable and hence, the system design needs to consider these differences. To investigate how the system designs need to change when the participating users have only limited observations, we focus on a simple social norm with binary reputation labels but allow adjusting the punishment severity through randomization. First, we model the belief heterogeneity using a suitable Bayesian belief function. Next, we formalize the users' optimal decision problems and derive in which scenarios they follow the prescribed social strategy. With this result, we then study the system dynamics and formally define equilibrium in the sense that the system is stable when users strategically optimize their decisions. By rigorously studying two specific cases where users' belief distribution is constant or is linearly influenced by the true reputation distribution, we prove that the optimal reputation update rule is to choose the mildest possible punishment. This result is further confirmed for higher order beliefs in simulations., 24 pages, 9 figures

This paper develops a game-theoretic framework for the design and analysis of a new class of incentive schemes called intervention schemes. We formulate intervention games, propose a solution concept of intervention equilibrium, and prove its existence in a finite intervention game. We apply our framework to resource sharing scenarios in wireless communications, whose non-cooperative outcomes without intervention yield suboptimal performance. We derive analytical results and analyze illustrative examples in the cases of imperfect and perfect monitoring. In the case of imperfect monitoring, intervention schemes can improve the suboptimal performance of non-cooperative equilibrium when the intervention device has a sufficiently accurate monitoring technology, although it may not be possible to achieve the best feasible performance. In the case of perfect monitoring, the best feasible performance can be obtained with an intervention scheme when the intervention device has a sufficiently strong intervention capability., 29 pages, 1 table, 6 figures

Large-scale multimedia semantic concept detection requires realtime identification of a set of concepts in streaming video or large image datasets. The potentially high data volumes of multimedia content, and high complexity associated with individual concept detectors, have hindered the practical deployment of many current solutions. In this paper, we present a summary of our work in building systems and applications for resource adaptive semantic concept detection in multimedia using large-scale distributed stream mining systems. We construct such concept detection applications as a hierarchical topology of individual concept detectors, and deploy them on distributed processing infrastructure. We then focus on dynamically configuring individual concept detectors to meet system imposed resource constraints while minimizing a penalty defined in terms of the misclassification cost. We present multiple centralized and distributed algorithms for this configuration, and describe the implemented application and system. We also verify through simulations that significant improvement in terms of accuracy of classification can be achieved through our approach.

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 discuss various network 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. To quantify the utility benefit derived by exchanging different information, we define a new metric referred to as the value of information. Simulation results show the improvements that can be achieved when various information is exchanged between users, and discuss the required complexity involved in implementing the various resource negotiation strategies.

Multi-user 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 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 the Nash bargaining solution (NBS) for our resource management problem. We provide interpretations for the NBS for multi-user resource allocation: the NBS can be used to maximize the system quality. 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.

With the emergence of shared overlay network infrastructures and the recent deregularization of spectrum policies, a new, more dynamic network resource "market" is emerging. To effectively operate this new market, resource management becomes of paramount importance. This is especially important for multimedia streaming applications that require a large amount of resources to guarantee an acceptable level of multimedia quality to the end users. However, providing the necessary resources to various networked multimedia users is challenging since they have different requirements in terms of multimedia characteristics, delay, or network constraints. To simplify this problem, we propose a novel utility-based resource management scheme for multi-user multimedia transmission over networks. To manage the available resources, the resource manager deploys bargaining solutions from economics in order to explicitly consider the utility impact for different resource allocation schemes. We focus on the Kalai-Smorodinsky bargaining solution (KSBS) because it can successfully model relevant noncollaborative utility-aware fairness policies for multimedia users. The KSBS explicitly considers the application-specific utility domain (i.e., resulting multimedia quality) when performing the resource allocation. The proposed KSBS allocates the resources in such a way that the achieved utility of every participating station incurs the same quality penalty, i.e., the same decrease in video quality as opposed to their maximum achievable qualities. Our simulations show that the proposed game-theoretic resource management provides a fairer and more efficient allocation of resources in terms of derived multimedia quality.

This paper presents the design of MPEG-4 contents streaming system that provides object-based interactivity over IP networks, and shows the execution results of the proposed streaming system. The proposed streaming system deploys overlay network to support application-level multicast.

Cross protocol layer optimizations have been recently proposed for improving the performance of real-time video transmission over 802.11 WLANs. However, performing such cross-layer optimizations is difficult since the video data and channel characteristics are time-varying, and analytically deriving the relationships between quality and channel characteristics given delay and power constraints is difficult. Furthermore, these relationships are often non-linear and non-deterministic (only worst or average case values can be determined). Complex Lagrangian or multi-objective optimization problems are thus often faced. In this paper, we propose a novel framework for solving cross MAC-application layer optimization problems. More specifically, we employ classification techniques to find an optimized cross-layer strategy for wireless multimedia transmission. Our solution deploys both content- and channel-related features to select a joint application-MAC strategy from the different strategies available at the various layers. Preliminary results indicate that considerable improvements can be obtained through the proposed cross-layer techniques relying on classification as opposed to ad-hoc solutions. The improvements are especially important at high packet-loss rates (5% and higher), where deploying a judicious mixture of strategies at the various layers becomes essential.

This paper addresses the problem of efficient and robust transmission of video over a hierarchical wireless infrastructure. First, we compress the data generated by the video sources using a multi-resolution wavelet coder. Subsequently, the generated bitstreams are transmitted over a wireless infrastructure to multiple regional processing centers (RPC), which then send the distorted bitstreams to a common higher-level processing center/receiver. We propose a symmetric framework for the joint compression and error protection of the correlated video bitstreams received by the RPC. We show that significantly improved video performance can be obtained by our framework over a variety of channel conditions.

We discuss the benefits of using realistic rate-distortion-complexity (R-D-C) models to guide ubiquitous multimedia streaming systems. At the server or proxy, on-the-fly bitstream adaptation to varying network conditions and diverse end-devices' processing capabilities can be performed by relying on accurate (pre-computed) R-D-C models. To accommodate various types of receivers with different resources, we introduce the concept of generic complexity metrics (GCMs) which quantify in a generic manner the decoding complexity of a compressed video sequence. A state-of-the-art motion-compensated wavelet-based coding scheme is used to illustrate how R-D-C models can be computed for a particular compression scheme. Finally, we propose a receiver-driven streaming framework and show how R-D-C models can be used to assist real-time wired/wireless multimedia transmission applications.

Advances in Internet technology and provision of constantly increasing bandwidth capabilities to end-users, have increased the need for streaming media services that can match the quality of traditional TV broadcast and offer enhanced user interactivity. In this paper, the target services, as well as specific implementation details of a scalable interactive multimedia streaming platform named ISMuS is presented. The platform is capable of providing high quality streaming service based on MPEG-4 encoded streams, through the use of scalable video coding technology and a media distribution architecture based on overlay network technology

Wireless devices currently operate in a non-collaborative manner that limits their performance and the overall communication system performance, as the competing stations do not always effectively exploit the available resources. We propose to fundamentally change the non-collaborative way in which wireless stations currently interact, by allowing them to exchange information and resources to improve the performance of wireless multimedia applications. In this paper, we introduce several new fairness concepts for wireless multimedia systems that allow information and resource exchanges among stations, and show their advantages as compared to existing fairness mechanisms used in wireline communications.

To enable video transmission over heterogeneous wireless networks, a highly scalable compression and streaming framework that can adapt to large and rapid bandwidth variations in real-time is necessary. MPEG-4 fine grained scalability (FGS) provides fine-grained SNR and temporal scalabilities, but these scalabilities are implemented and performed independently, thereby neglecting the gains that can be made from making joint SNR-temporal decisions to maximize quality. A novel fine grained SNR-temporal scalability framework called FGS+ that provides a new level of performance by considering SNR and temporal scalability jointly is presented. This new solution uses the results of our subjective tests, which indicate the levels to which SNR-quality needs to be enhanced before motion-smoothness should be improved. The study also reveals that these SNR-temporal tradeoff points vary among videos, and depend on the characteristics of the video. Based on these observations, our solution uses reference frames, enhanced relative to FGS, for prediction, improving visual quality over MPEG-4 FGS by up to 1.5 dB.

This paper presents a systematic approach to establish the complexity scalability of MPEG-4 Fine-Granular-Scalability (FGS) to fulfill the requirements of Universal Multimedia Access (UMA). We also present receiver-driven streaming strategies using the proposed FGS-based system.

MPEG-4 and H.26L have reached or neared 'international standard' status and applications are being developed based on these standards. The new H.26L standard has been proven to be an attractive choice for videostreaming over MPEG-4. However, just as in single layer MPEG-4, it lacks adaptability to bandwidth variations. This paper examines FGS-like systems for H.26L.

The universal multimedia access (UMA) paradigm is aimed at enabling multimedia data to be accessed by a large number of users/clients at any time, and from anywhere, based on their preferences, communication channel characteristics and device capabilities. For the implementation of the UMA framework, "universal" scalable video coding techniques that enable adaptive video transmission based on these three parameters are essential. Fine-granular-scalability (FGS) has recently been adopted as the MPEG-4 standard for the efficient and flexible distribution of multimedia over heterogeneous wired and wireless networks. While it has already been proven that FGS fulfils the first two UMA requirements (adaptive and robust transmission based on the channel characteristics and user preferences) due to its bit-rate scalability, the ability of FGS to provide device-based complexity-scalability was hitherto unproven. This paper proposes using an information theoretic framework, based on rate-distortion-complexity theory, for evaluating bit-rate and complexity scalable coding techniques for UMA. In this context, the ability of FGS to provide bit-rate and complexity scalability that allows easy and smooth navigation across the operational rate-distortion-complexity surface is demonstrated. Moreover, a comparison with non-scalable MPEG schemes is also given. Furthermore, we illustrate how this framework can be used in an FGS-based resource-driven video streaming system.

Fine-granularity-scalability (FGS) has recently been standardized in MPEG-4 due to its flexibility in adapting in real-time to Internet bandwidth variations and its resilience to packet-losses. However, the flexibility and robustness come at the expense of degrading video quality when compared with non-scalable MPEG-4 video coding at a given bit-rate. To reduce this visual quality penalty at low and medium bit-rates, the "Frequency Weighting" (FW) method has been standardized that allows the prioritized transmission of "low frequency" DCT coefficients. In this paper, we propose a novel scene-characteristic-dependent adaptive FW method aimed at improving the visual quality of FGS. After a thorough analysis of the FGS (i.e., SNR) residual signal at various bit-rates, we conclude that for an improved subjective quality, different FW matrices should be used to improve the FGS visual quality depending on the video sequence characteristics. Subsequently, a simple classification mechanism is developed that categorizes the video sequences based on their brightness, motion and texture activity in four distinct classes, each using a different FW matrix. For each class, the appropriate FW matrix was determined a priori based on the differences of the residual signals for two representative single-layer bit-rates. This adaptive FW (AFW) method has been subjectively evaluated and shows a clear improvement in visual quality compared with non-frequency weighted or non-adaptive frequency weighted sequences.

Fine-granularity-scalability (FGS) has recently been adopted by the MPEG-4 standard for the efficient and flexible distribution of multimedia over the Internet. The FGS framework is able to perform spatio-temporal-SNR tradeoffs in real-time at transmission or (post-) processing time, depending upon the available bit-rate, sequence characteristics, priority, user preferences or receiver resources, and not at encoding time. This paper investigates the usage of FGS for robust video transmission over wireless networks. To cope with the detrimental channel conditions, we introduce a novel joint source-channel coding scheme that combines FGS and adaptive modulation. Two schemes are presented, one uses hierarchical modulation and FGS and the other uses time division multiplexing and FGS. Both schemes are shown by computer simulation to maintain a stable video quality over a variety of multipath dynamic Rayleigh fading channels. The proposed adaptive modulated FGS algorithms provide a graceful degradation and improvement in picture quality as the channel conditions vary.

The MPEG-4 fine-granular-scalability (FGS) has been introduced as a flexible low-complexity solution for video streaming over heterogeneous networks (eg, the Internet). A novel scalable video-coding framework and corresponding compression methods for wireless video streaming are introduced. Building upon the FGS approach, the proposed framework, which we refer to as motion-compensation FGS (MC-FGS), provides improved video quality of up to 2 dB.

Transmission of video over bandwidth-varying networks (e.g. the Internet) requires a highly scalable solution that is capable of adapting to the network condition in real-time. To address this requirement, scalable video coding schemes with multiple enhancement-layers have been proposed. However, under this multiple-layer paradigm, the transmission bitrate of each layer has to be predetermined at encoding time. Consequently, the range of bitrates that can be covered with these compression schemes is limited and often lower-than or different from the desired range required at transmission time. In this paper, a novel scalable video-coding framework and a corresponding compression method for Internet video streaming is introduced. Building upon the MPEG-4 fine-granular-scalability (FGS) approach, the proposed framework provides a new level of abstraction between the encoding and transmission-process by supporting both SNR and temporal scalability through a single enhancement layer. Consequently, our proposed approach enables streaming systems to support full SNR, full temporal, and hybrid SNR-temporal scalability in real-time depending on available bandwidth, packet-loss patterns, user preferences, and/or receiver complexity.

We evaluate the packet loss resilience of the MPEG-4 Fine-Granular-Scalability (FGS) video coding method for Internet streaming applications. Since unrecoverable packet losses are very common over the Internet, the focus of this study is to determine how robust the MPEG-4 FGS coding tool is under unrecoverable IP packet-losses over a wide range of connection qualities (i.e. at various bit-rates). Our extensive study was performed upon an entire set of sequences, bit-rates and packet-loss rates. As shown in this paper, under unequal packet-loss protection, FGS provides a clear advantage over non-scalable coding. If equal packet-loss protection is employed, FGS performance is still considerably better, in particular under moderate-to-high packet-loss ratios.