Q-LVS: A Q-Learning-based Algorithm for Video Streaming in Peer-to-Peer Networks Considering a Token-Based Incentive Mechanism

The peer-to-peer video streaming has reached great attention during the recent years. Video streaming in the peer-to-peer networks is a good way to stream video on the Internet due to the high scalability, high video quality, and low bandwidth requirements. In this paper, the issue of live video streaming in the peer-to-peer networks that contain selfish peers is addressed. In order to encourage the peers to cooperate in video distribution, tokens are used as an internal currency. The tokens are gained by the peers when they accept requests from other peers to upload video chunks to them, and tokens are spent when sending requests to other peers to download video chunks from them. In order to handle the heterogeneity in the bandwidth of peers, the assumption has been made that the video is coded as multi-layered. For each layer, the same token has been used but priced differently per layer. Based on the available token pools, the peers can request various qualities. A new token-based incentive mechanism has been proposed that adapts the admission control policy of the peers according to the dynamics of the request submission, request arrival, time to send requests, and bandwidth availability processes. The peer-to-peer requests could arrive at any time, so the continuous Markov decision process has been employed.