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 videou0027s 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 timevarying. 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 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 that can be modelled as piecewise CBR channels. Experimental results show the good performance of our proposed algorithm.

A novel framework for improving bandwidth utilization for VBR video delivery over widearea networks There are great challenges in streaming variablebitrate video over widearea networks due to the significant variation of network conditions. The utilization of the precious bandwidth of widearea networks is often low in such streaming systems. In this paper, we propose a novel framework to improve the bandwidth utilization from a new perspective. Instead of focusing on the performance of each single media stream, we aim to improve the overall bandwidth utilization for video streaming systems. We try to exploit the unoccupied bandwidth in ongoing streams and using it to deliver some prefetched data which can be used to facilitate future streaming. Preliminary results show that our mechanism has great potential to improve both the overall bandwidth utilization and the caching performance of the proxy servers in the streaming systems.