Priori Knowledge Guided Approach for Optimal Peer Selection in P2P VoD Systems

With the rise of Video-on-Demand (VoD) systems as a preferred way to distribute video content over IP networks, many research works and innovations have focused on improving the scalability of streaming systems by looking at distributed approaches such as peer-to-peer (P2P). One of the most critical aspects in P2P-assisted streaming system is the real-time resource allocation, which drives the performance of the system in terms of capacity utilization and VoD requests rejection rates. In this paper, we specifically focus on the problem of maximizing the P2P streaming system utilization by effectively alternating between different resource allocation strategies. Switching between different resource allocation strategies is guided by a run-time statistical analysis of performances against predicted content popularity pattern. A key contribution of this paper resides in effectively combining different, and potentially conflicting, performance objectives when deciding on which resource allocation strategy to use. Indeed, we use a Bayesian Fusion to select the most appropriate resource allocation strategy to deal with future content demand. With our P2P resource allocation framework, a VoD service operator can combine any number of resource allocation strategies and formulate different performance objectives that meet the requirements of its network and the content consumption behavior of its users.