An efficient shared learning scheme for layered video streaming over application layer multicast

Abstract

Layered video multicast such as RLM (Receiver-driven layered multicast) is a promising technique for delivering streaming video to a set of heterogeneous receivers over ALM (Application Layer Multicast) as well as over IP multicast. However, this approach may suffer from unnecessary fluctuation of video quality due to overlapped and failed join-experiments. Though a shared learning scheme was introduced to resolve these problems in IP multicast-based layered video streaming, it may cause high control overhead and slow convergence problem when used with ALM. In this thesis, we propose a new shared learning scheme for ALM-based layered video multicast which reduces control overhead and convergence latency while keeping the number of fluctuation reasonably small. Through the analysis of link sharing in ALM, we redefine the range of receivers that are affected by overlapped or failed join-experiments and incorporate it into the proposed scheme. The simulation results show that the proposed scheme performs better than an ALM-based layered video multicast with shared learning in terms of control overhead and convergence latency. The real-world experiment results on Emulab show that our proposed scheme outperforms the ALM-based approach with respect to control overhead and convergence latency.