Evolutionary games for cooperative P2P video streaming
The wide-spread use of P2P video streaming systems have introduced a large number of unnecessary traverse links leading to substantial network inefficiency. To address this problem and achieve better streaming performance, we propose to enable cooperation among group peers, which are geographically neighboring peers with large intra-group upload and download bandwidths. Considering the peers' selfish nature, we formulate the cooperative streaming problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) for every peer. Moreover, we propose a simple and distributed learning algorithm for the peers to converge to the ESSs. Compared to the traditional non-cooperative P2P schemes, the proposed cooperative scheme achieves much better performance in terms of social welfare and probability of real-time streaming.