Incentive analysis for cooperative interactive multiview video streaming

Abstract

In interactive multiview video streaming (IMVS), users can periodically select one out of many captured views available for observation. In single-view video streaming, cooperative strategies where peers share received packets of the same video have proven to be effective in reducing server׳s upload burden, and incentive mechanisms are designed to stimulate user cooperation. However, exploiting user cooperation in higher dimensional IMVS is difficult, since users watching different views makes it difficult to establish partnership, and users switching views frequently and independently makes it difficult to maintain partnership over time. In this paper, we use a multiview video frame structure for IMVS to support cooperative view-switching, where peers may help each other even if they are observing different views. We then model peers׳ interaction as an indirect reciprocity game, where each user is assigned a reputation level. To gain a higher reputation level, users help others, which in turn leads to a higher likelihood to receive others׳ help later. In this work, we focus on how view switching, the key feature of IMVS, affects user cooperation. By modeling users׳ decision making as a Markov decision process, our analysis shows that users tend to cooperate at some views but not others: given peers can predict their future view navigation paths probabilistically, for a peer who is likely to enter a view-switching path not requiring others׳ help, he also has less incentive to cooperate. Furthermore, we observe that the game may have multiple Nash Equilibria corresponding to different cooperation levels, e.g., users cooperate at all views in the full cooperation equilibrium, while users only cooperate at certain views in the partial cooperation equilibrium. The particular equilibrium the game will converge to depends on the initial cooperation level of the game. To stimulate user cooperation at all views, we propose a Pay-for-Cooperation (PfC) scheme at the beginning of the game to drive the game to the full cooperation equilibrium to improve system efficiency. Our simulation results show the effectiveness of PfC.