Distributed Optimization for Video Communications

1. Distributed Optimization for Video Communications Ryerson Multimedia research Lab (RML), Ryerson University, Toronto, Canada Server Throughput Maximization for Scalable P2P VoD Systems Problem: Maximize: aggregate throughput Subject to: source rate constraint download bandwidth constraint upload bandwidth constraint link-forwarding constraint Peer Client/server VoD Cannot serve a large number of users Distributed algorithm: message exchange with neighborsonly in IEEE Trans. MM, vol. 11, no. 3, 2009 Server Server 1 Streaming Capacity Problem Streaming capacity: the maximal streaming rate that can be received by every user Problem: Maximize: streaming rate Subject to: equal rate received by users download bandwidth constraint upload bandwidth constraint 2 3 4 5 Peer 6 The streaming capacity Vs. server upload bandwidth P2P VoD Graph in Proc. of IEEE ISCAS 2009 and ICME 2009 Scalable architecture Optimal PrefetchingFramework in P2P VoD Systems in IEEE Trans. TMM , vol. 11, no. 1, 2009 (joint work with Microsoft Research Asia) Optimal prefetching framework Determine which segments to prefetch Guided seek Network Lifetime Maximization for Wireless Visual Sensor Networks Optimized Video Multicasting over Wireless Ad Hoc Networks Optimized Video Multicasting over Wireless Ad Hoc Networks in IEEE Trans. CSVT, vol. 19, no. 5, 2009 in IEEE Trans. CSVT, vol. 19, no. 6, 2009 Problem: Maximize: network lifetime Subject to: network flow conservation video quality requirement Problem: Maximize: video quality received by users Subject to: network flow conservation wireless channel capacity constraint transmission power constraint Visual sensor Source Video quality comparison Power consumption at each sensor User 2 User 1 Sink These projects are supported in part by Canada Research Chair Program, Canada Foundation for Innovation. Last Updated on Oct. 22, 2009