Multimedia Streaming Rate Optimization in Peer-to-peer Network

Abstract

The recent advancement in compression techniques and networking technologies have resulted in wide deployment of novel content distribution applications. These applications enable the end-users to have ubiquitous access to media streaming services such as live broadcasting, video-on-demand, and video conferencing. Peer-to-peer (P2P) has become the most popular means for media distribution because it provides an application-layer platform that can abstract the underlying network heterogeneity. However, the lack of dedicated bandwidth means that P2P systems must implement proper resource-allocation techniques to optimally utilize available bandwidth.

In the thesis, we present bandwidth optimization algorithms for two types of media streams: Continuous and Scalable. In continuous stream, we present an optimization algorithm based on Convex Optimization framework. For scalable stream, we present a distributed message-passing framework for optimization using sum-product update algorithm. Advantage of this approach over existing heuristic-based algorithm is that the optimization algorithm itself is independent of the underlying constraints. The algorithm iteratively updates resource allocation decision based on a given set of codewords. The codewords are binary representation of various network and video constraints. Therefore, any number of constraints can be used to generate a set of codewords without modifying the algorithm.