Optimizing segment caching for peer-to-peer on-demand streaming

In peer-to-peer (P2P) on-demand streaming applications, multimedia content is divided into segments and peers can seek any segments for viewing at anytime. Since different segments may be of different popularity, random segment caching would lead to a segment popularity-supply mismatch, and hence an uneven workload distribution among peers. Some popular segments may be far from peers, leading to inefficient search and streaming. In this paper, we study optimal segment caching for P2P on-demand streaming. We first state the segment caching optimization problem, and propose a centralized heuristic to solve it, which serves as a benchmark for other algorithms. We then propose a distributed caching algorithm termed POPCA (popularity-based caching algorithm), in which each peer adaptively and independently replaces segments to minimize the popularity-supply discrepancy and the segment distance from peers. Through simulations, we show that POPCA achieves near-optimal performance, and lower peer workload and segment distance as compared with other schemes.