A simple model for chunk-scheduling strategies in P2P streaming

Abstract

Peer-to-peer (P2P) streaming tries to achieve scalability (like P2P file distribution) and at the same time meet real-time playback requirements. It is a challenging problem still not well understood. In this paper, we describe a simple stochastic model that can be used to compare different downloading strategies to random peer selection. Based on this model, we study the tradeoffs between supported peer population, buffer size, and playback continuity. We first study two simple strategies: Rarest First (RF) and Greedy. The former is a well-known strategy for P2P file sharing that gives good scalability by trying to propagate the chunks of a file to as many peers as quickly as possible. The latter is an intuitively reasonable strategy to get urgent chunks first to maximize playback continuity from a peer's local perspective. Yet in reality, both scalability and urgency should be taken care of. With this insight, we propose a Mixed strategy that achieves the best of both worlds. Furthermore, the Mixed strategy comes with an adaptive algorithm that can adapt its buffer setting to dynamic peer population. We validate our analytical model with simulation. Finally, we also discuss the modeling assumptions and the model's sensitivity to different parameters and show that our model is robust.