Enhancing QoE of WebRTC-based Video Conferencing using Deep Reinforcement Learning

Abstract

The proportion of video traffic within the total internet traffic is steadily increasing and then video traffic already accounts for over half of the internet traffic. The increase in video traffic is due to the growing number of users for video-related services such as video streaming, live streaming, and video telephony. With the increasing users on video-related services, the importance of the quality of experience (QoE) for these services will become even more crucial in the future. Numerous studies to enhance the experience quality of video streaming have been conducted using adaptive bitrate (ABR) algorithms and artificial intelligence (AI). However, this work focuses on a more complex problem: improving the experience quality in multi-party, bidirectional communication scenarios such as video conferences. We propose a system that applies deep reinforcement learning (DRL) to the media server of a webRTC-based video conferencing system to allocate a bitrate’s video stream that suits the network conditions for users. The proposed method was implemented and evaluated, demonstrating great improvements. When the network conditions changed dynamically, the proposed approach achieved approximately 56.2% higher video bitrate compared to existing methods, resulting in a 24.7% enhancement in user experience quality.