Optimal reverse frame selection for stored video delivery under constrained resources

In this paper, we present an optimal reverse frame selection (RFS) algorithm based on dynamic programming for delivering stored video under both bandwidth and buffer size constraints. Our objective is to find a feasible set of frames that can maximize the video's accumulated motion metrics without violating any constraint. We further extend RFS to solve the problem of video delivery over VBR channels where the channel bandwidth is both limited and time-varying. In particular, we first run RFS offline for several bandwidth samples, and the computation complexity is modest and scalable with the aids of frame size stuffing and non-optimal state elimination. During online streaming, we only need to retrieve the optimal frame selection path from the pregenerated offline results, and it can be applied to any VBR channels that can be modelled as piecewise CBR channels. Experimental results show the good performance of our proposed algorithm.