Dynamic Programming-Based Reverse Frame Selection for VBR Video Delivery Under Constrained Resources

In this paper, we investigate optimal frame-selection algorithms based on dynamic programming for delivering stored variable bit rate (VBR) 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 values without violating any constraint. It is well known that dynamic programming has high complexity. In this research, we propose to eliminate nonoptimal intermediate frame states, which can effectively reduce the complexity of dynamic programming. Moreover, we propose a reverse frame selection (RFS) algorithm, where the selection starts from the last frame and ends at the first frame. Compared with the conventional dynamic programming-based forward frame selection, the RFS is able to find all of the optimal results for different preloads in one round. We further extend the RFS scheme to solve the problem of frame selection for VBR channels. In particular, we first perform the RFS algorithm offline, and the complexity is modest and scalable with the aids of frame stuffing and nonoptimal 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 as long as the VBR channels can be modeled as piecewise CBR channels. Experimental results show good performance of our proposed algorithms