ALVS: Adaptive Live Video Streaming using deep reinforcement learning. (September 2022)

• Record Type: Journal Article
• Authors: Ozcelik, Ihsan Mert; Ersoy, Cem
• Is Part Of: Journal of network and computer applications. Volume 205(2022)
• Publication Date: 2022-09
• Subjects: Adaptive playback speed -- Deep reinforcement learning -- Live streaming media and video quality; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2022.103451
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Achieving a high Quality of Experience (QoE) in live event streaming is a challenging problem given a low-latency requirement and time-varying network conditions. Adaptive video bitrate and adaptive playback speed techniques are two separate control knobs to address this challenge. In this paper, we consider these two control parameters in a joint optimization problem and present a deep reinforcement learning (DRL) framework to maximize QoE for live streaming without any assumption about the environment or fixed rule-based heuristics. With the proposed DRL framework, our approach (ALVS) constructs the inference model to make a joint decision of adaptive playback speed and video quality level for the next video segment. Simulation results through real network traces show that ALVS outperforms both state-of-the-art DRL-based and rule-based algorithms in terms of QoE without sacrificing live latency and skipping any content.