视频流媒体中源端基本层最佳速率确定算法

精细可扩展(fine grained scalable, FGS)编码是网络视频流行的编码技术之一. 采用FGS编码时, 基本层的速率选择需要在受益客户数量和视频编码效率之间进行折中. 为此, 本文提出了一个源端基本层最佳速率确定算法, 以最大化所有客户的观感体验. 论文首先提出了一个通过实验和曲线拟合建立 R-Q (Rate-Quality)函数曲线的方法, 在此基础上给出了缩小最佳基本层速率搜索空间的定理, 并针对单序列和多序列的不同特点和不问题复杂度, 提出了不同的算法. 针对单视频序化问题, 提出了改进的FGSOPT_N算法, 算法只用到了序列的R-Q函数曲线; 针对多视频序列问题是NP困难问题的实际, 提出了改进的定界MFGSOPT_N算法和改进的贪婪算法MFGS_N以确保找到最优基本层速率, 减少算法运行时间. 针对每种算法, 给出了实例进行算法效果的演示和对比. 结果显示提出的算法能可靠找到最优基本层速率.

The selection algorithm for the optimal base layer rate in stream media system

The fine grained scalable (FGS) coding is one of the most popular coding technologies in network video stream. In FGS stream, the base layer rate is an important parameter and should generally make a compromise between the number of beneficial clients and the efficiency of video coding. To maximize all clients' perceived quality, the selection algorithm for the optimal base layer rate is proposed. This paper first puts forward a method to build the R-Q (Rate-Quality) function curve and proposes an algorithm to cut search space of the base layer rate. Second, because the different feature and different problem complexity of single and multiple sequences, the different algorithms are proposed. According to the optimization problem of single sequence, the improved FGSOPT_N algorithm is proposed, which only bases on R-Q function curve of a sequence and according to the fact that the optimization problem of multiple sequences is NP hard, the improved MFGSOPT_N algorithm and greedy algorithm MFGS_N are proposed to improve the probability to find the optimal base layer rate, thus to reduce the runtime. At last, for each algorithm, illustration examples are given to demonstrate and compare the effect of algorithm. The results show the algorithms can reliably find the optimal base layer rate if it exits.