AP-assisted adaptive video streaming in wireless networks with high-density clients

Abstract

Adaptive video streaming over wireless networks has experienced tremendous growth in past few years. In order to guarantee users’ quality of experience (QoE), adaptive bitrate (ABR) algorithms have been extensively studied. With the recent emergency of high-density Wi-Fi networks, these solutions no longer perform well. On the one hand, bitrate decisions respond slowly to high network fluctuations, and on the other hand, wireless channel resources are insufficient especially under the cases that multiple clients compete for the limited bandwidth. Hence, users’ personalized QoE metrics need to be taken into account to cope with the QoE degradation. To this end, we design an access point (AP) assisted wireless dynamic adaptive video streaming over Hypertext Transfer Protocol (HTTP) solution called Wi-DASH, which aims to improve users’ QoE while considering channel utilization. In Wi-DASH, the video server aggregates real-time network status with the assistance of AP, and estimates clients’ player status through statistical analysis of chunk request logs. On this basis, a deep reinforcement learning (DRL) based ABR algorithm is designed, where the DRL model can deal with the complicated global status information including network status, player status, and QoE preferences. Finally, we implement Wi-DASH system, and conduct experiments with 4K resolution videos. Experimental results reveal that the Wi-DASH can more fully utilize wireless channel resources, and significantly improve users’ QoE.