While peer-to-peer (P2P) video streaming systems have achieved promising results, they introduce a large number of unnecessary traverse links, leading to substantial network inefficiency. To address this problem, we discuss how to enable cooperation among“group peers,” which are geographically neighboring peers with large intragroup upload and download bandwidths. Considering the peers’ selfish nature, we formulate the cooperative streaming problem as an evolutionary game and introduce, for every peer, the evolutionarily stable strategy (ESS). Moreover, we discuss a simple and distributed learning algorithm for the peers to converge to the ESSs. With the discussed algorithm, each peer decides whether to be an agent who downloads data from the peers outside the group or a free-rider who downloads data from the agents by simply tossing a coin, where the probability of the coin showing a head is learned from the peer’s own past payoff history. Simulation results show that compared to the traditional noncooperative P2P schemes, the discussed cooperative scheme achieves much better performance in terms of social welfare, probability of real-time streaming, and video quality.