Markov Decision Model for Perceptually Optimized Video Scheduling

Transmitting video over slow fading wireless channels with good perceptual quality is a challenging task because no time-diversity can be exploited to combat channel variations, especially when the frequency diversity and spatial diversity is not available due to the wireless system implementation. While quality-scalable video coding techniques make video source-rate adaptation possible, determining a good scheduling strategy which selectively schedules video data associated with different layers is a challenging problem. For the best performance of a wireless video system, the scheduler needs to consider the channel state, the buffer state and the perceptual video quality at the receiver. In this paper, we propose a scheduling algorithm to optimize the perceptual quality of scalably coded videos transmitted over slow fading channels. By modeling the dynamics of the channel as a Markov chain, we reduce the problem of dynamic video scheduling to a tractable Markov decision problem over a finite state space. We then employ an infinite-horizon average-reward maximization algorithm to maximize the time-average Multi-Scale Structural SIMilarity (MS-SSIM) index which has been shown to correlate highly with human judgments of video quality. Simulation results show that the proposed MDP-based scheduling policy achieves significant perceptual quality improvement over scheduling methods which do not explicitly exploit the channel dynamics. Furthermore, we propose an on-line scheduling method which not only performs nearly as well as the MDP-based performance but also has very low implementation complexity.