Rate adaptation is one of the most important issues in dynamic adaptive streaming over HTTP (DASH). Due to the frequent fluctuations of the network bandwidth and complex variations of video content, it is difficult to deal with the varying network conditions and video content perfectly by using a single rate adaptation method. In this paper, we propose an ensemble rate adaptation framework for DASH, which aims to leverage the advantages of multiple methods involved in the framework to improve the quality of experience (QoE) of users. The proposed framework is simple yet very effective. Specifically, the proposed framework is composed of two modules, i.e., the method pool and method controller. In the method pool, several rate adaptation methods are integrated. At each decision time, only the method that can achieve the best QoE is chosen to determine the bitrate of the requested video segment. Besides, we also propose two strategies for switching methods, i.e., InstAnt Method Switching, and InterMittent Method Switching, for the method controller to determine which method can provide the best QoEs. Simulation results demonstrate that, the proposed framework always achieves the highest QoE for the change of channel environment and video complexity, compared with state-of-the-art rate adaptation methods.
Bibliographical noteThis work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0831003, in part by the National Natural Science Foundation of China under Grant 61571274 and Grant 61871342, in part by the Shandong Natural Science Funds for Distinguished Young Scholar under Grant JQ201614, and in part by the Young Scholars Program of Shandong University under Grant 2015WLJH39.
- Dynamic adaptive streaming over HTTP (DASH)
- quality of experience (QoE)
- rate adaptation
- video compression
- video transmission