Scalable Video Multicasting: A Stochastic Game Approach With Optimal Pricing

Abstract

Heterogeneous multimedia content delivery over wireless networks is an important yet challenging issue. One of the challenges is maintaining the quality of service due to scarce resources in wireless communications and heavy loadings from heterogeneous demands. A promising solution is combining multicasting and scalable video coding (SVC) techniques via cross-layer design, which has been shown to effectively enhance the quality of multimedia content delivery service in the literature. Nevertheless, most existing works on SVC multicasting system focus on the static scenarios, where a snapshot of user demands is given and remains the same. In addition, the economic value of the SVC multicasting system, which is an important issue from the service provider's perspective, has seldom been explored. In this paper, we study a subscription-based SVC multicasting system with stochastic user arrival and heterogeneous user preferences. A stochastic framework based on the multidimensional Markov decision process (M-MDP) is proposed to study the negative network externality existing in the proposed system and theoretically evaluate the corresponding system efficiency. A game-theoretic analysis is conducted to understand the rational demands from heterogeneous users under different subscription pricing schemes. By transforming the original dynamic and complex M-MDP revenue optimization problem into a traditional average-reward MDP problem, we show that the optimal pricing strategy that maximizes the expected revenue of the service provider can be derived efficiently. Moreover, the overall user's valuation on the system, e.g., social welfare, is maximized under such an optimal pricing strategy. Finally, the efficiency of the proposed solutions is evaluated through simulations.