基于缓存补偿的视频码率自适应算法

针对基于超文本传输协议（HTTP）的动态自适应流（DASH）码率自适应算法未能充分利用视频缓存以及平均码率偏低的问题，提出一种DASH标准的基于缓存补偿的码率自适应切换（BASBC）算法。首先，根据最近下载分片的下载速率分析带宽波动程度并得到预估带宽；其次，依据预估带宽和当前码率等级在缓存区设置码率上切阈值和码率下切阈值，并利用动态上切阈值控制码率向上切换，消耗缓存时长，而利用动态下切阈值控制码率向下逐级切换，累积缓存时长，从而在缓存区形成累积-消耗的缓存状态循环。BASBC算法在视频播放平均码率上高于动态自适应的HTTP流码率渐进切换（DASBS）算法，有效提高了带宽利用率；虽然所提算法的平均码率稍低于基于DASH标准的码率平滑切换（RSS）算法，但所提算法的码率切换更为平滑，整体切换稳定性表现更优。实验结果表明，所提算法在动态网络环境中具有高带宽利用、切换平滑且稳定的良好表现，能够有效提高用户的体验质量（QoE）。

Buffer compensation based video bitrate adaptation algorithm

Concerning the problem that the existing bitrate adaptation algorithms based on Dynamic Adaptive Streaming over Hyper Text Transfer Protocol （HTTP） （DASH） have low buffer utilization and low average bitrate， a Bitrate Adaptive Switching based on Buffer Compensation （BASBC） algorithm based on DASH standard was proposed. Firstly， the bandwidth fluctuation was analyzed according to the download speeds of the recent downloaded fragments， and thereby obtaining the estimated bandwidth. Secondly， according to the estimated bandwidth and current bitrate level， bitrate higher/lower switching thresholds were set in the buffer to control bitrate switching to higher level and consum buffer time， or to control bitrate switching down gradually and accumulate buffer time， respectively， so that a consumption-accumulation buffer state loop was formed in video buffer. The average bitrate of video playback of BASBC algorithm is higher than that of Dynamic Adaptive Step-wise Bitrate Switching （DASBS） algorithm for HTTP streaming， thereby effectively improving the bandwidth utilization. Although the average bitrate of the proposed algorithm is slightly lower than that of Rate Smooth Switching （RSS） algorithm based on DASH standard， the smoothness of bitrate switching of the proposed algorithm is better， improving the switching stability. Experimental results show that the proposed algorithm has good performance such as high bandwidth utilization， switching smoothness and switching stability in dynamic network environment， which can effectively improve the Quality of Experience （QoE） of users.