Incentive analysis for cooperative distribution of interactive multiview video

In interactive multiview video streaming (IMVS), users can periodically select one out of many captured views available for observation as video is played back in time. In single-view video streaming, to reduce server´s upload burden, cooperative strategies where peers share received packets of the same video have proven to be effective, and incentive mechanisms are designed to stimulate user cooperation. Exploiting user cooperation in high dimensional IMVS, however, is more challenging. First, small number of peers in a local area are likely watching different views among large number of views available, making it difficult for a peer to find partners of the exact same view to cooperate. Second, even if a peer can identify cooperative partners of the same view, they will soon be watching different views after independent view-switching. In this paper, we study the use of a multiview video frame structure for IMVS that facilitates cooperative view switching, where even if peers are observing different views, they can nonetheless help each other. To stimulate user cooperation, we model peers´ interaction as an indirect reciprocity game. Using Markov decision process (MDP) as a formalism, each peer makes distributed decisions to maximize his aggregate utilities within his lifetime. Simulation results show that when the cost to help others is much smaller than the utility gained from others´ help, users fully cooperate. As the cost-to-gain ratio increases, users tend to behave differently at different views: given peers can predict their future view navigation paths probabilistically, a peer likely to enter a view-switching path not requiring others´ help will have less incentive to cooperate. When the cost-to-gain ratio is very large, no users will cooperate.