Two-Phase Rate Adaptation Strategy for Improving Real-Time Video QoE in Mobile Networks  

作　　者：Ailing Xiao Jie Liu Yizhe Li Qiwei Song Ning Ge 

机构地区：[1]Department of Electronic Engineering, Tsinghua University, Beijing 100084, China [2]Beijing National Research Center for Information Science and Technology, Beijing 100084, China [3]Network Technology Research Institute, China Unicom, Beijing 100052, China

出　　处：《China Communications》2018年第10期12-24,共13页中国通信（英文版）

基　　金：supported by the National Nature Science Foundation of China(NSFC 60622110,61471220,91538107,91638205);National Basic Research Project of China(973,2013CB329006),GY22016058

摘　　要：With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users＇ quality of experience（Qo E） has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users＇ real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors（video quality, rebuffering, and rate change） and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase： the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.

关 键 词：continuous quality of experience （QoE） model recurrent neural network（RNN） real-time video QoE improving dynamic adaptive streaming over HTTP （DASH） 

分 类 号：TN919.8[电子电信—通信与信息系统]