H.264/SVC parameter optimization based on quantization parameter, MGS fragmentation, and user bandwidth distribution

Abstract

In the situation of limited bandwidth, how to improve the performance of scalable video coding plays an important role in video coding. The previously proposed scalable video coding optimization schemes concentrate on reducing coding computation or trying to achieve consistent video quality; however, the connections between coding scheme, transmission environments, and users' accesses manner were not jointly considered. This article proposes a H.264/SVC (scalable video codec) parameter optimization scheme, which attempt to make full use of limited bandwidth, to achieve better peak signal-to-noise ratio, based on the joint measure of user bandwidth range and probability density distribution. This algorithm constructs a relationship map which consists of the bandwidth range of multiple users and the quantified quality increments measure, QP e , in order to make effective use of the video coding bit-stream. A medium grain scalability fragmentation optimization algorithm is also presented with respect to user bandwidth probability density distribution, encoding bit rate, and scalability. Experiments on a public dataset show that this method provides significant average quality improvement for streaming video applications.

H.264 Scalable Video Coding Transmission on MIMO without CSI Feedback

“…This makes choosing the right QP an essential part of the encoding process. In [10], an H.264/SVC parameter optimization based on quantization parameter and user bandwidth distribution was proposed using the bandwidth range and probability density distribution. In [11], on the other hand, the authors proposed an effective algorithm to calculate the suitable QP value with low complexity.…”