PROJECT TITLE :
Distributed Robust Optimization for Scalable Video Multirate Multicast Over Wireless Networks
This paper proposes a distributed strong optimization theme to jointly optimize overall video quality and traffic performance for scalable video multirate multicast over sensible wireless networks. In order to guarantee layered utility maximization, the initial nominal joint supply and network optimization is outlined, where each scalable layer is ready-made 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 methods disjoint from the primal methods. It considers the path-overlapping allocation of backup ways for various receivers to require advantage of network coding, and takes into consideration the robust multipath rate-control and bandwidth reservation drawback for scalable video multicast streaming when doable link failures of primary ways exist. Specifically, an uncertainty set of the wireless medium capacity is introduced to represent the uncertain and time-varying property of parameters connected to the wireless channel. The targeted uncertainty within the robust optimization drawback is studied during a form of protection functions with nonlinear constraints, to investigate the tradeoff between robustness and distributedness. Using the twin decomposition and primal-twin update approach, we tend to develop a totally decentralized algorithm with regard to communication overhead. Through in depth experimental results beneath crucial performance factors, the proposed algorithm could converge to the optimal steady-state a lot of quickly, and adapt the dynamic network changes in an optimal tradeoff between optimization performance and robustness than existing optimization schemes.
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