Junni Zou

Optimal Power Allocation for Hybrid Overlay/Underlay Spectrum Sharing in Multiband Cognitive Radio Networks

In this paper, we consider power allocation in multiband cognitive radio (CR) networks, where multiple secondary users (SUs) transmit via a common relay and compete for the transmit power of the relay. We employ a hybrid overlay/underlay spectrum sharing scheme, allowing the SU to adapt its way of accessing the licensed spectrum to the status of the primary user (PU). If the PU is detected to be idle at the selected channel, the SU works in an overlay mode; else, it works in spectrum underlay. In addition, an auction-based power-allocation scheme is proposed to solve power competition of multiple SUs. For the SU working in spectrum overlay, the relay allocates the power in proportion to its payment without additional constraints; for the SU in spectrum underlay, its own transmit power and that of the relay are upper bounded for the quality of service (QoS) of the PU. Then, the convergence of the proposed auction algorithm and the outage probability of secondary transmissions is theoretically analyzed. Finally, the performance of the proposed scheme is verified by the simulation results.

Joint Coding/Routing Optimization for Distributed Video Sources in Wireless Visual Sensor Networks

This paper studies a joint coding/routing optimization between network lifetime and video distortion by applying information theory to wireless visual sensor networks for correlated sources. Arbitrary coding [distributed video coding and network coding (NC)] from both combinatorial optimization and information theory could make significant progress toward the performance limit and tractable. Also, multipath routing can spread energy utilization across nodes within the entire network to keep a potentially longer lifetime, and solve the wireless contention issues by the splitting traffic. The objective function not only keeps the total energy consumption of encoding power, transmission power, and reception power minimized, but ensures the information received by sink nodes to approximately reconstruct the visual field. Also, a generalized power consumption model for distributed video sources is developed, in which the coding complexity of Key frames and Wyner-Ziv frames is measured by translating specific coding behavior into energy consumption. On the basis of the distributed multiview video coding and NC-based multipath routing, the balance problem between lifetime (costs) and distortion (capacity) is modeled as an optimization formulation with a fully distributed solution. Through a primal decomposition, a two-level optimization is relaxed with Lagrangian dualization and solved by the gradient algorithm. The low-level optimization problem is further decomposed into a secondary master dual problem with four cross-layer subproblems: a rate control problem, a channel contention problem, a distortion control problem, and an energy conservation problem. The implementation of the distributed algorithm is discussed with regard to the communication overhead and dynamic network change. Simulation results validate the convergence and performance of the proposed algorithm.

Lifetime and Distortion Optimization With Joint Source/Channel Rate Adaptation and Network Coding-Based Error Control in Wireless Video Sensor Networks

In this paper, we study joint performance optimization on network lifetime and video distortion for an energy-constrained wireless video sensor network (WVSN). To seek an appropriate tradeoff between maximum network lifetime and minimum video distortion, a framework for joint source/channel rate adaptation is proposed, where the video encoding rate, link rate, and power consumption are jointly considered, formulating a weighted convex optimization problem. In the context of lossy wireless channels, an efficient error control scheme that couples network coding and multipath routing is explored. Moreover, an integrated power consumption model, including power dissipation on video compression, error control, and data communication, is specifically developed for the video sensor node. By primal decomposition, the original problem is decomposed into a two-level optimization procedure, with the high-level procedure for source adaptation (source rate optimization) and the low-level procedure for channel adaptation (network resource allocation). Finally, a fully decentralized iterative algorithm is developed to resolve the target optimization problem. Extensive simulation results evaluate the convergence performance of the proposed algorithm and demonstrate the best tradeoff performance.

Distributed Robust Optimization for Scalable Video Multirate Multicast Over Wireless Networks

This paper proposes a distributed robust optimization scheme to jointly optimize overall video quality and traffic performance for scalable video multirate multicast over practical wireless networks. In order to guarantee layered utility maximization, the initial nominal joint source and network optimization is defined, where each scalable layer is tailored in an incremental order and finds jointly optimal multicast paths and associated rates with network coding. To enhance the robustness of the nominal convex optimization formulation with nonlinear constraints, we reserve partial bandwidth for backup paths disjoint from the primal paths. It considers the path-overlapping allocation of backup paths for different receivers to take advantage of network coding, and takes into account the robust multipath rate-control and bandwidth reservation problem for scalable video multicast streaming when possible link failures of primary paths exist. Specifically, an uncertainty set of the wireless medium capacity is introduced to represent the uncertain and time-varying property of parameters related to the wireless channel. The targeted uncertainty in the robust optimization problem is studied in a form of protection functions with nonlinear constraints, to analyze the tradeoff between robustness and distributedness. Using the dual decomposition and primal-dual update approach, we develop a fully decentralized algorithm with regard to communication overhead. Through extensive experimental results under critical performance factors, the proposed algorithm could converge to the optimal steady-state more quickly, and adapt the dynamic network changes in an optimal tradeoff between optimization performance and robustness than existing optimization schemes.

Joint Source and Flow Optimization for Scalable Video Multirate Multicast Over Hybrid Wired/Wireless Coded Networks

This paper aims to optimize the overall video quality and traffic performance for multi-rate video multicast over hybrid wired/wireless networks. In order to perform layered utility maximization over tiered networks, we propose a joint source-network flow optimization scheme where individual layers of the scalable video stream are imposed on their optimal multicast paths and associated rates for the highest sustainable layered video quality with minimum costs. It sufficiently guarantees that each destination node accesses progressive layered stream in an incremental order, considers network coding across overlapping paths to destination nodes for decent multicast capacity, and addresses the link contention problem during wireless transmission. We formulate the problem into convex programming with the objective to minimize the total rate-distortion variations between layers. Using primal decomposition and the primal-dual approach, we develop a decentralized algorithm with two levels of optimization. The numerical and packet-level results compare extensive performance under different control conditions over coded and non-coded hybrid networks. It demonstrates that the proposed algorithm could actually achieve the max-flow throughput and provide better video quality with optimal layered access for heterogeneous receivers.

Auction-Based Power Allocation for Multiuser Two-Way Relaying Networks

The overall performance of a cooperative relaying system largely depends on power allocation schemes. In this paper, we address the power allocation problem in a network-coded multiuser two-way relaying network, where multiple pair users communicate with their partners via a common relay node, and compete for the transmit power of the relay. An auction-based power allocation scheme is proposed, in which two users in each pair bid as a single player for a maximum utility of the whole pair, and share the total pair payment in proportion to the amount of power they obtained. The convergence of the proposed auction game (i.e., the convergence to a unique Nash Equilibrium) is theoretically proved by using a standard function. Moreover, the outage behavior is systematically analyzed and a closed form of the pair outage probability is derived. Finally, the performance of the proposed scheme is verified by simulation results.

Joint Coding/Routing Optimization for Correlated Sources in Wireless Visual Sensor Networks

This paper studies a joint coding/routing optimization between network lifetime and rate-distortion, by applying information theory to wireless visual sensor networks for correlated sources. Arbitrary coding (distributed source coding and network coding) from both combinatorial optimization and information theory could make significant progress towards the performance limit of information networks and tractable. Also, multipath routing can spread energy utilization across nodes within the entire network to keep a potentially longer lifetime, and solve the wireless contention issues by the splitting traffic. The objective function not only keeps a total energy consumption of encoding power, transmission power, and reception power minimized, but ensures the information received by sink nodes to approximately reconstruct the visual field. Based on the localized Slepian-Wolf coding and network coding-based multipath routing, the balance problem between distortion (capacity) and lifetime (costs) is modeled as an optimization formulation with a distributed solution. Through a primal decomposition, a two-level optimization is relaxed with Lagrangian dualization and solved with the gradient algorithm. The low-level optimization problem is decomposed into a secondary master dual problem (encoding, energy, and congestion prices update) with four cross-layer subproblems: a rate control problem, a channel contention problem, a distortion control problem, and an energy conservation problem. Numerical results validate the convergence and performance of the proposed algorithm.

Dynamic Spectrum Access and Power Allocation for Cooperative Cognitive Radio Networks

In the existing cooperative cognitive radio, primary users are generally assumed to be more than capable of supporting their target throughput, thereby the rate enhancement or the cooperation from secondary users actually has less attraction to primary users. Instead, they might be more interested in the benefits in other format. This paper presents a new cooperative cognitive radio framework, where primary users are willing to cooperatively relay data for secondary users with their under-utilized resource to earn the revenue. An auction model with multiple auctioneers, multiple bidders and hybrid divisible/indivisible commodities is proposed to solve channel bands and cooperative transmit power competitions among secondary users. For maximizing the utility, secondary users can select receiving primary users assistance (i.e., work in cooperative transmission mode) and purchase both spectrum and power from the primary user, or select direct transmission mode and just buy spectrum from the primary user. Then, the convergence of the proposed auction scheme to a Walrasian equilibrium is mathematically proved. Finally, the performance of the proposed scheme is verified by the simulation results.

Prioritized Flow Optimization With Multi-Path and Network Coding Based Routing for Scalable Multirate Multicasting

In this paper, we study performance optimization for scalable video coding and multicast over networks. Multi-path video streaming, network coding based routing, and network flow control are jointly optimized to maximize a network utility function defined over heterogeneous receivers. Content priority of video coding layers is considered during the flow routing to determine the optimal multicast paths and associated data rates for each layer. Our optimization scheme attempts to find content distribution meshes with minimum path costs for each video coding layer while satisfying the inter-layer dependency during scalable video coding. Based on primal decomposition and primal-dual analysis, we develop a decentralized algorithm with two optimization levels to solve the performance optimization problem. We also prove the stability and convergence of the proposed iterative algorithm using Lyapunov theories. Extensive experimental results demonstrate that the proposed algorithm not only achieves the max-flow throughput using network coding, but also provides better video quality with balanced layered access for heterogeneous receivers.

Joint bandwidth allocation, data scheduling and incentives for scalable video streaming over peer-to-peer networks

The overall performance of a peer-to-peer (P2P) scalable streaming system largely depends on the strategies employed in bandwidth allocation, data scheduling and incentives. In this paper, we develop a credit-based content-aware bandwidth auction model for scalable streaming in P2P networks. It formulates multi-overlay multi-layer bandwidth request and allocation problems as auction games. Each peer in the games acts as both auctioneer and player. Being a auctioneer, it maximizes the total revenue (credits) by selling upload bandwidth; Being a player, it uses the credits earned in bandwidth sales to sequentially bid for layer bandwidth so as to maximize the received video quality. Also, a content-aware bidding strategy is proposed, under which the required bandwidth quantity from a peer is determined by the informative video chunks and the marginal net utility that peer could provide, as well as the available credits and the maximum layer bit rate. The convergence of the proposed auction algorithm is mathematically proved. Finally, the performance of the proposed scheme is verified by simulation results.