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Pre-existing immune responses towards adenoviral vector limit the use of a vector based on particular serotypes and its clinical applicability for gene therapy and/or vaccination. Therefore, there is a significant interest to vectorize novel adenoviral types that have low seroprevalence in the human population. Here, we describe the discovery and vectorization of a chimeric human adenovirus, which we call HAdV-20-42-42. Full genome sequencing revealed that this virus is closely related to human serotype 42, except for the penton-base which is derived from serotype 20. The HAdV-20-42-42 vector could be propagated stably to high titers on existing E1-complementing packaging cell lines. Receptor binding studies revealed that the vector utilized both CAR and CD46 as receptors for cell entry. Furthermore, the HAdV-20-42-42 vector was potent in transducing human and murine cardiovascular cells and tissues, irrespective of the presence of blood coagulation factor X. In addition, the vector did not sequester in the liver upon intravenous administration in rodents. Finally, we demonstrate that potent T-cell responses against vector-delivered antigens could be induced upon vaccination. In summary, from the data obtained we conclude that HAdV-20-42-42 provides a valuable addition to the portfolio of adenoviral vectors available to develop safe and efficacious products in the fields of gene therapy and vaccination.IMPORTANCEAdenoviral vectors are currently under investigation for a broad range of therapeutic indications in diverse fields, such as oncology and gene therapy, as well as for vaccination both for human and veterinary use. A wealth of data shows that pre-existing immune responses may limit the use of a vector. Particularly in the current climate of global pandemic, there is a need to expand the toolbox with novel adenoviral vectors for vaccine development. Our data demonstrates that we have successfully vectorized a novel adenovirus serotype with low seroprevalence. The cell transduction data and antigen-specific immune responses induced in vivo demonstrate that this vector is highly promising for the development of gene therapy and vaccine products.

Preexisting immune responses toward adenoviral vectors limit the use of a vector based on particular serotypes and its clinical applicability for gene therapy and/or vaccination. Therefore, there is a significant interest in vectorizing novel adenoviral types that have low seroprevalence in the human population. Here, we describe the discovery and vectorization of a chimeric human adenovirus, which we call HAdV-20-42-42. Full-genome sequencing revealed that this virus is closely related to human serotype 42, except for the penton base, which is derived from serotype 20. The HAdV-20-42-42 vector could be propagated stably to high titers on existing E1-complementing packaging cell lines. Receptor-binding studies revealed that the vector utilized both CAR and CD46 as receptors for cell entry. Furthermore, the HAdV-20-42-42 vector was potent in transducing human and murine cardiovascular cells and tissues, irrespective of the presence of blood coagulation factor X. In vivo characterizations demonstrate that when delivered intravenously (i.v.) in mice, HAdV-20-42-42 mainly targeted the lungs, liver, and spleen and triggered robust inflammatory immune responses. Finally, we demonstrate that potent T-cell responses against vector-delivered antigens could be induced upon intramuscular vaccination in mice. In summary, from the data obtained we conclude that HAdV-20-42-42 provides a valuable addition to the portfolio of adenoviral vectors available to develop efficacious products in the fields of gene therapy and vaccination. IMPORTANCE Adenoviral vectors are under investigation for a broad range of therapeutic indications in diverse fields, such as oncology and gene therapy, as well as for vaccination both for human and veterinary use. A wealth of data shows that preexisting immune responses may limit the use of a vector. Particularly in the current climate of global pandemic, there is a need to expand the toolbox with novel adenoviral vectors for vaccine development. Our data demonstrate that we have successfully vectorized a novel adenovirus type candidate with low seroprevalence. The cell transduction data and antigen-specific immune responses induced in vivo demonstrate that this vector is highly promising for the development of gene therapy and vaccine products.

Since the first studies on bowhead whale singing behaviour, song variations have been consistently reported. However, there has been little discussion regarding variability in bowhead whale singing display and its ecological significance. Unlike the better studied humpback whales, bowhead whales do not appear to share songs at population level, but several studies have reported song sharing within clusters of animals. Over the winter season 2013–2014, in an unstudied wintering ground off Northeast Greenland, 13 song groups sharing similar hierarchical structure and units were identified. Unit types were assessed through multidimensional maps, showing well separated clusters corresponding to manually labelled units, and revealing the presence of unit subtypes. Units presented contrasting levels of variability over their acoustic parameters, suggesting that bowhead whales keep consistency in some units while using a continuum in values of frequency, duration and modulation parameters for other unit types. Those findings emphasise the need to account for variability in song analysis to better understand the behavioural ecology of this endangered species. Additionally, shifting from song toward units or phrase-based analysis, as it has been suggested for humpback whales, offers the opportunity to identify and track similarities in songs over temporal and geographical scales relevant to population monitoring.

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.

The enterovirus genus of the picornavirus family includes a large number of important human pathogens such as poliovirus, coxsackievirus, enterovirus-A71, and rhinoviruses. Like all other positive-strand RNA viruses, genome replication of enteroviruses occurs on rearranged membranous structures called replication organelles (ROs). Phosphatidylinositol 4-kinase IIIβ (PI4KB) is required by all enteroviruses for RO formation. The enteroviral 3A protein recruits PI4KB to ROs, but the exact mechanism remains elusive. Here, we investigated the role of Acyl-coenzyme A binding domain containing 3 (ACBD3) in PI4KB recruitment upon enterovirus replication using ACBD3-knockout (ACBD3KO) cells. ACBD3 knockout impaired replication of representative viruses from four enterovirus and two rhinovirus species. PI4KB recruitment was not observed in the absence of ACBD3. The lack of ACBD3 also affected the localization of individually expressed 3A, causing 3A to localize to the endoplasmic reticulum instead of the Golgi. Reconstitution of wt ACBD3 restored PI4KB recruitment and 3A localization, while an ACBD3 mutant that cannot bind to PI4KB restored 3A localization, but not virus replication. Consistently, reconstitution of a PI4KB mutant that cannot bind ACBD3 failed to restore virus replication in PI4KBKO cells. Finally, by reconstituting ACBD3 mutants lacking specific domains in ACBD3KO cells, we show that Acyl-coenzyme A binding (ACB) and charged amino acids region (CAR) domains are dispensable for 3A-mediated PI4KB recruitment and efficient enterovirus replication. Altogether, our data provide new insight into the central role of ACBD3 in recruiting PI4KB by enterovirus 3A and reveal the minimal domains of ACBD3 involved in recruiting PI4KB and supporting enterovirus replication.ImportanceAs all other RNA viruses, enteroviruses reorganize host cellular membranes for efficient genome replication. A host lipid kinase, PI4KB, plays an important role on this membrane rearrangement. The exact mechanism of how enteroviruses recruit PI4KB was unclear. Here, we revealed a role of a Golgi-residing protein, ACBD3, as a mediator of PI4KB recruitment upon enterovirus replication. ACBD3 is responsible for proper localization of enteroviral 3A proteins in host cells which is important for 3A to recruit PI4KB. By testing ACBD3 and PI4KB mutants that abrogate the ACBD3-PI4KB interaction, we showed that this interaction is crucial for enterovirus replication. The importance of specific domains of ACBD3 was evaluated for the first time, and the essential domains for enterovirus replication were identified. Our findings open up a possibility for targeting ACBD3 or its interaction with virus as a novel strategy for a broad-spectrum antiviral drug.

The hydrophobic molecules of the metabolome - also named the lipidome - constitute a major part of the entire metabolome. Novel technologies show the existence of a staggering number of individual lipid species, the biological functions of which are, with the exception of only a few lipid species, unknown. Much can be learned from pathogens that have evolved to take advantage of the complexity of the lipidome to escape the immune system of the host organism and to allow their survival and replication. Different types of pathogens target different lipids as shown in interaction maps, allowing visualization of differences between different types of pathogens. Bacterial and viral pathogens target predominantly structural and signaling lipids to alter the cellular phenotype of the host cell. Fungal and parasitic pathogens have complex lipidomes themselves and target predominantly the release of polyunsaturated fatty acids from the host cell lipidome, resulting in the generation of eicosanoids by either the host cell or the pathogen. Thus, whereas viruses and bacteria induce predominantly alterations in lipid metabolites at the host cell level, eukaryotic pathogens focus on interference with lipid metabolites affecting systemic inflammatory reactions that are part of the immune system. A better understanding of the interplay between host-pathogen interactions will not only help elucidate the fundamental role of lipid species in cellular physiology, but will also aid in the generation of novel therapeutic drugs.

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.

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.

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.

We propose and analyze a broad family of games played by resource-constrained players, which are characterized by the following central features: 1) each user has a multi-dimensional action space, subject to a single sum resource constraint; 2) each user's utility in a particular dimension depends on an additive coupling between the user's action in the same dimension and the actions of the other users; and 3) each user's total utility is the sum of the utilities obtained in each dimension. Familiar examples of such multi-user environments in communication systems include power control over frequency-selective Gaussian interference channels and flow control in Jackson networks. In settings where users cannot exchange messages in real-time, we study how users can adjust their actions based on their local observations. We derive sufficient conditions under which a unique Nash equilibrium exists and the best-response algorithm converges globally and linearly to the Nash equilibrium. In settings where users can exchange messages in real-time, we focus on user choices that optimize the overall utility. We provide the convergence conditions of two distributed action update mechanisms, gradient play and Jacobi update., Comment: 39 pages, 5 figures

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.

In communication systems where users share common resources, users' selfish behavior usually results in suboptimal resource utilization. There have been extensive works that model communication systems with selfish users as one-shot games and propose incentive schemes to achieve Pareto optimal action profiles as non-cooperative equilibria. However, in many communication systems, due to strong negative externalities among users, the sets of feasible payoffs in one-shot games are nonconvex. Thus, it is possible to expand the set of feasible payoffs by having users choose convex combinations of different payoffs. In this paper, we propose a repeated game model generalized by intervention. First, we use repeated games to convexify the set of feasible payoffs in one-shot games. Second, we combine conventional repeated games with intervention, originally proposed for one-shot games, to achieve a larger set of equilibrium payoffs and loosen requirements for users' patience to achieve it. We study the problem of maximizing a welfare function defined on users' equilibrium payoffs, subject to minimum payoff guarantees. Given the optimal equilibrium payoff, we derive the minimum intervention capability required and design corresponding equilibrium strategies. The proposed generalized repeated game model applies to various communication systems, such as power control and flow control., 42 pages, 7 figures, 2 tables

In this paper, we propose a systematic solution to the problem of scheduling delay-sensitive media data for transmission over time-varying wireless channels. We first formulate the dynamic scheduling problem as a Markov decision process that explicitly considers the users' heterogeneous multimedia data characteristics (e.g., delay deadlines, distortion impacts and dependences, and so on) and time-varying channel conditions, which are not simultaneously considered in state-of-the-art packet scheduling algorithms. This formulation allows us to perform foresighted decisions to schedule multiple data units for transmission at each time in order to optimize the long-term utilities of the multimedia applications. The heterogeneity of the media data enables us to express the transmission priorities between the different data units as a priority graph, which is a directed acyclic graph. This priority graph provides us with an elegant structure to decompose the multidata unit foresighted decision at each time into multiple single-data unit foresighted decisions which can be performed sequentially, from the high priority data units to the low priority data units, thereby significantly reducing the computation complexity. When the statistical knowledge of the multimedia data characteristics and channel conditions is unknown a priori, we develop a low-complexity online learning algorithm to update the value functions, which capture the impact of the current decision on the future utility. The simulation results show that the proposed solution significantly outperforms existing state-of-the-art scheduling solutions.

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.

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.

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.

This paper considers a noncooperative game in which competing users sharing a frequency-selective interference channel selfishly optimize their power allocation in order to improve their achievable rates. Previously, it was shown that a user having the knowledge of its opponents' channel state information can make foresighted decisions and substantially improve its performance compared with the case in which it deploys the conventional iterative water-filling algorithm, which does not exploit such knowledge. This paper discusses how a foresighted user can acquire this knowledge by modeling its experienced interference as a function of its own power allocation. To characterize the outcome of the multiuser interaction, the conjectural equilibrium is introduced, and the existence of this equilibrium for the investigated water-filling game is proven. Importantly, we show that both the Nash equilibrium and the Stackelberg equilibrium are special cases of the conjectural equilibrium. We also develop practical algorithms to form accurate beliefs and select desirable power allocation strategies. Numerical simulations indicate that a foresighted user without any a priori knowledge of its competitors' private information can effectively learn how the other users will respond to its actions, and induce the entire system to an operating point that improves both its own achievable rate as well as the rates of the other participants in the water-filling game.

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.

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.

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 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.

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.

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 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.

The objective of dynamic voltage scaling (DVS) is to adapt the frequency and voltage for configurable platforms to obtain energy savings. DVS is especially attractive for video decoding systems due to their time-varying and highly complex workload and because the utility of decoding a frame is solely depending on the frame being decoded before its display deadline. Several DVS algorithms have been proposed for multimedia applications. However, the prior work did not take into account the video compression algorithm specifics, such as considering the temporal dependencies among frames and the required display buffer. Moreover, the effect of the passive (leakage) power when performing DVS for multimedia systems was not explicitly considered. In this paper, we determine the optimal scheduling of the active and passive states to minimize the total energy for video decoding systems. We pose our problem as a buffer-constrained optimization problem with a novel, compression-aware definition of processing jobs. We propose low-complexity algorithms to solve the optimization problem and show through simulations that significant improvements can be achieved over state-of-the art DVS algorithms that aim to minimize only the active power.

In this paper, we propose a new resource allocation framework for multimedia systems that perform multiple simultaneous video decoding tasks. We jointly consider the available system resources (e.g., processor cycles) and the video decoding task's characteristics such as the sequence's content, the bit-rate, and the group of pictures (GOP) structure, in order to determine a fair and optimal resource allocation. To this end, we derive a quality-complexity model that determines the quality [in terms of peak signal-to-noise ratio (PSNR)] that a task can achieve given a certain system resource allocation. We use these quality-complexity models to determine a quality-fair and Pareto-optimal resource allocation using the Kalai-Smorodinski Bargaining Solution (KSBS) from axiomatic bargaining theory. The KSBS explicitly considers the resulting multimedia quality when performing a resource allocation and distributes quality-domain penalties proportional to the difference between each video decoding task's maximum and minimum quality requirements. We compare the KSBS with other fairness policies in the literature and find that, because it explicitly considers multimedia quality, it provides significantly fairer resource allocations in terms of the resulting PSNR compared with policies that operate solely in the resource domain. To weight the quality impact of the resource allocations to the different decoding tasks depending on application-specific requirements or user preferences, we generalize the existing KSBS solution by introducing bargaining powers based on each video sequence's motion and texture characteristics.