  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 adap tation 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.
