Multiple description coded video streaming in peer-to-peer networks
Li, Xue Jun.
Date of Issue2012
School of Electrical and Electronic Engineering
It is known that in a peer-to-peer (P2P) network a peer node serves as both a receiver and a supplier, which enables uploading bandwidth of peer nodes to be utilized efficiently while relieving burden of the server node. This solves the scalability problem typically encountered in the traditional client–server model. However, frequent peer churn and varying bandwidth of peer nodes in P2P networks pose significant challenges for video streaming. These challenges can be addressed from both the P2P system design and the advanced video coding perspectives. In this paper, we first present a survey on the existing P2P video streaming systems that leverage the multiple description coding (MD coding or MDC) techniques, featured in providing strong error resilience for video delivery and supporting heterogeneity for peer nodes. Compared with layered coded video streaming, MD coded video streaming presents stronger robustness without requiring special provisions in P2P system design at a modest cost of compression efficiency, which is desirable in dynamic and error-prone P2P networks. In the MD coded video streaming, packet scheduling is critical to performance of mesh-based P2P systems. A new packet scheduling framework is formulated for receiver-driven MD coded video streaming, where a receiver collects peer nodes' information and generates a transmission schedule for MDC packets. In the proposed framework, a rate-distortion optimized packet selection scheme is developed to minimize the expected distortion subject to limited downloading bandwidth. Accordingly a rate-distortion based prioritized peer selection scheme is employed to choose an appropriate peer node for each of the selected packets. Simulation results validate the effectiveness of the proposed scheduling scheme and the advantage of MDC over layered coding in a network with frequent peer churn.
DRNTU::Engineering::Electrical and electronic engineering
Signal processing: image communication
© 2012 Elsevier B.V.