Multi-Hypothesis Rate Adaptation Framework For Dynamic Adaptive Streaming Over HTTP

With the rapid development of the Internet,and the demand for media applications is increasing.Therefore,it is important to improve the video streaming service and the quality of experience(QoE)of users.Dynamic Adaptive Streaming over HTTP(DASH)can be widely used to provide users with continuous video streaming services in a dynamic network environment.Rate adaptation is one of the most important issues in dynamic adaptive streaming over HTTP that is becoming more and more popular for network video transmission.Due to the frequent fluctuations of network bandwidths and complex variations of video contents,it is difficult to deal with the varying network conditions and video contents perfectly by using a single rate adaptation method.In this paper,we propose a multi-hypothesis rate adaptation framework for DASH,which aims to make use of the advantages of multiple methods that are integrated into the framework,and thus improving the quality of experience of users under varying channel conditions and video complexities.Specifically,the proposed framework is composed of two modules,i.e.method-pool and method controller.In the method-pool,several rate adaptation methods are integrated.At each decision time,only one method that can achieve the best QoE that is defined by taking video quality,quality variation,buffer length,and rebuffering into account is chosen to determine the bitrate of the requested video segment.Besides,we also propose two method switching strategies,i.e.,InstAnt Method Switching(IAMS),and InterMittent Method Switching(IMMS),for the proposed framework.Simulation results demonstrate the effectiveness of the proposed framework.By comparing with state-of-the-art rate adaptation methods,the proposed framework can always achieve the largest QoE for the change of channel environment and video complexity.