A multi-objective particle swarm optimization data scheduling algorithm for peer-to-peer video streaming

In P2P (Peer-to-Peer) video streaming systems using unstructured mesh, data scheduling is an important factor on system performance. An optimal data scheduling scheme should achieve two objectives ideally. The first objective is to optimize the perceived video quality of peers. The second objective is to maximize the network throughput, i.e., utilize the upload bandwidth of peers maximally. However, the optimized perceived video quality may not bring a maximized network throughput, and vice versa. In the paper, to better achieve the two objectives simultaneously, we formulate the data scheduling problem as a multi-objective optimization problem. To solve the multi-objective optimization problem, we propose a multi-objective particle swarm optimization data scheduling algorithm by encoding the peers' neighbors as the locations of the particles. Through simulations, we demonstrate the proposed algorithm outperforms other algorithms in terms of the perceived video quality and the utilization of peers' upload capacity.