Adaptive learning for scalable video transmission with HARQ over dynamic wireless channels

This paper addresses the problem of dynamic, real-time video transmission control over a time-varying wireless channel. The problem of adaptive source coding control is studied on the basis of scalable video coding schemes. A video-layer-based hybrid automatic repeat request scheme is adopted to achieve adaptive error protection allocation. The problem of joint source-channel resource allocation over a time-varying wireless channel is posed as a constrained Markov Decision Process (MDP). The goal of the proposed video streaming MDP is to minimize the average end-to-end frame distortion with the constraint on the average transmission time for each layer. In order to address the issue of unknown channel dynamics and inaccurate distortion model, the R-learning-based on-line algorithm is adopted for learning the transmission police. To handle the constrained MDP with the standard R-learning algorithm, the constrained MDP is converted into an unconstrained MDP through the Lagrangian approach. The efficiency and convergence of the proposed learning algorithm are demonstrated with the simulation results.