Improving The Performance Of HTTP Adaptive Streaming

Video traffic is prominent in recent years,including video-on-demand(Vo D)streaming and live streaming.To meet the increasing user demands on quality-of-experience(QoE),most content providers use HTTP adaptive streaming(standardized as DASH)to deliver video.In DASH,video is split into chunks encoded into multiple bitrate levels.Client-side adaptive bitrate(ABR)algorithm dynamically adjusts the bitrate at chunk boundaries.The bitrate is selected based on throughput prediction and buffer occupancy with the goal of maximizing QoE.For Vo D streaming,high QoE means high bitrate,low rebuffering and low bitrate switch.For live streaming,high QoE means low delay,low rebuffering and high bitrate.However,picking a correct bitrate is challenging since(1)in the highly variable network conditions where both the throughput and buffer occupancy change rapidly,the video bitrate can be switched frequently,and it is hard to obtain an accurate throughput prediction;(2)the video chunks at the same quality have various chunk sizes;(3)Optimizing QoE for live streaming requires the ABR algorithm to control the buffer occupancy at a small but non-empty level,which is challenging in the highly variable network.This thesis investigates the bitrate adaptation issue for both two kinds of video streaming in detail.(1)We propose a multi-step prediction based scheme to reduce bitrate switches.Existing ABR algorithms rely on instantaneous system state to select bitrate,which can induce serious bitrate switches in unstable networks.To improve the bitrate smoothness,we propose to use multi-step prediction to make bitrate selection and develop the MSPC scheme.Experiment results show that MSPC is able to effectively reduce the bitrate switches,with the video bitrates only adapting to sustainable network changes while ignoring the momentary fluctuations.(2)We analyze the characteristics of chunk size variation and its impact on QoE,and improve the bitrate adaptation to eliminate the negative impact.Through measurement experiments,we analyze the characteristics of chunk size variation and observe that it increases the risk of rebuffering.To eliminate this risk,we propose that the bitrate decision should be made based on the actual chunk size rather than the nominal bitrate.Experiment results demonstrate the effectiveness of the improved ABR algorithm.(3)We propose RBC algorithm by combining QoE optimization and buffer control to improve the robustness against throughput prediction errors.We observe that the sensitivity of existing ABR algorithms to throughput prediction error is resulted by the lack of buffer control.Since the bitrate selection only aims to optimize QoE without concerning the buffer dynamic,the buffer occupancy varies widely at great risk of rebuffering.To restrains the rebuffering problem induced by throughput prediction errors,we propose the RBC algorithm which controls the buffer occupancy within a safe range while making QoE optimization.By evaluation experiments,it shows that RBC can largely reduce the rebuffering time.(4)We use the hybrid of feedback control and feedforward control to develop HCA algorithm for live streaming which has strong control capability.Optimizing the QoE for low delay live streaming can be realized by controlling the buffer occupancy at a small but non-empty level.To achieve accurate control,we propose to combine the feedforward controller and the feedback controller and design a hybrid control-based algorithm HCA.Experiment results show that HCA is able to provide high bitrate and keep a very low rebuffering ratio within the low delay constraint.