QoE Aware Video Streaming Scheme Utilizing GRU-Based Bandwidth Prediction and Adaptive Bitrate Selection for Heterogeneous Mobile Networks

Nowadays, video streaming has become a popular form of multimedia for communication and entertainment. The rapid traffic explosion spurred by the development of emerging video-centric services such as e-science, virtual reality, and video conference has caused network congestion and the degradation in quality of experience (QoE), especially in heterogeneous mobile networks. To cope with that, the development of QoE-efficient video streaming solutions, i.e., HTTP adaptive video streaming, is critical. In this work, we investigate HTTP adaptive video streaming solutions that are capable of improving QoE for heterogeneous mobile networks in which the network conditions including bandwidth are significantly varied. We propose an effective QoE-aware adaptive bitrate video streaming scheme that integrates a bandwidth prediction based on Gated Recurrent Unit (GRU) neural networks with an adaptive bitrate selection strategy to wisely determine the suitable quality level for each video chunk. Thanks to the accurate bandwidth estimation and the adaptation of each video chunk bitrate to the network conditions, QoE metrics have been enhanced. Numerical experiments have been deployed to verify the performance of our proposed solution in comparison with that of the notable conventional methods. The attained simulation results demonstrate that the developed solution is significantly more effective than the conventional methods. The proposed method obtains a performance increment, in terms of QoE, of up to 19.4% compared to the conventional ones.