Distributed Robust Optimization for Scalable Video Multirate Multicast Over Wireless Networks
Distributed Robust Optimization for Scalable Video Multirate Multicast Over Wireless Networks
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DOI：
10.1109/tcsvt.2012.2186740
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发表时间：
2012-06
影响因子：
8.4
通讯作者：
Chenglin Li;H. Xiong;Junni Zou;Changwen Chen
中科院分区：
文献类型：
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作者：
Chenglin Li;H. Xiong;Junni Zou;Changwen Chen
提出了一种分布式鲁棒性优化方案，以联合优化实际无线网络上的可伸缩视频多速率组播的整体视频质量和流量性能。为了保证分层效用最大化，定义了初始标称联合源和网络优化，其中每个可扩展层以增量顺序定制，并找到联合最优多播路径和与网络编码相关联的速率。为了提高非线性约束的名义凸优化公式的鲁棒性，我们保留部分带宽的备份路径不相交的原始路径。它考虑了路径重叠分配的备份路径，为不同的接收机，以利用网络编码，并考虑到鲁棒的多径速率控制和带宽预留问题的可伸缩视频组播流时，可能存在的主要路径的链路故障。具体地说，无线介质容量的不确定性集被引入来表示与无线信道相关的参数的不确定性和时变特性。针对鲁棒优化问题中的目标不确定性，以非线性约束的保护函数的形式进行研究，分析鲁棒性和分布性之间的权衡。使用对偶分解和原始-对偶更新方法，我们开发了一个完全分散的算法方面的通信开销。在关键性能因素下的大量实验结果表明，与现有的优化方案相比，该算法能更快地收敛到最优稳态，并在优化性能和鲁棒性之间达到最优折衷，适应网络的动态变化。
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.