Optimal smoothness results and approximation techniques for real-time VBR video traffic smoothing

We study the problem of real time VBR video traffic smoothing. One fundamental difficulty in this problem is that at any time during transmission, the information of most future video frames is not available and thus it is unlikely that global optimization can be achieved in the smoothing process. In order to measure the effectiveness of real time video smoothing methods, we first propose a benchmark algorithm which achieves optimality on some of the smoothness parameters in the smoothing results. Based on this algorithm, we found that significant discrepancy exists between the results produced by some of the existing smoothing methods and the smoothness upper bounds. With this observation, we then focus on devising an algorithm which improves the smoothing results. Experimental results show that our algorithm makes noticeable improvements in some of the smoothness parameters compared to existing smoothing methods.