Foresighted joint resource reciprocation and scheduling strategies for real-time video streaming over peer-to-peer networks

We consider peer-to-peer (P2P) networks, where multiple heterogeneous and self-interested peers are sharing multimedia data. In this paper, we propose a novel scheduling algorithm for real-time video streaming over dynamic P2P networks. The proposed scheduling algorithm is foresighted, since it enables each peer to maximize its long-term video quality by efficiently utilizing its limited resources (e.g., uploading bandwidth) over time, while explicitly considering the time-varying resource reciprocation behaviors of its associated peers. To successfully design the scheduling algorithm, we consider a distinct buffer structure that allows the peers to model the resource reciprocation behavior as a reciprocation game. Then, each peer can determine its foresighted decisions based on a Markov Decision Process (MDP). The simulation results show that the proposed algorithm significantly improves the average video quality, compared to other existing scheduling strategies. Moreover, simulation results also show that the proposed algorithm can flexibly and effectively operate in heterogeneous P2P networks.