Application of active queue management for real-time adaptive video streaming

• Published in: Telecommunication systems Vol. 79; no. 2; pp. 261 - 270
• Main Authors: de Morais, Wladimir Gonçalves, Santos, Carlos Eduardo Maffini, Pedroso, Carlos Marcelo
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Buffers; Business and Management; Computer Communication Networks; Internet; IT in Business; Performance evaluation; Probability Theory and Stochastic Processes; Queuing theory; Real time; Signal to noise ratio; Streaming media; Telecommunications systems; Traffic congestion; Vibration; Video broadcasting; Video on demand; Video transmission; Video streaming; Multimedia communication; DASH; Live streaming; Active queue management
• ISSN: 1018-4864; 1572-9451
• DOI: 10.1007/s11235-021-00848-0

Video streaming currently dominates global Internet traffic. Live streaming broadcasts events in real-time, with very different characteristics compared to video-on-demand (VoD), being more sensitive to variations in delay, jitter, and packet loss. The use of adaptive streaming techniques over HTTP is massively deployed on the Internet, adapting the video quality to instantaneous condition of the network. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular adaptive streaming technology. In DASH, the client probes the network quality and adjusts the quality of requested video segment according to the bandwidth fluctuations. Therefore, DASH is an over-the-top application using unmanaged networks to distribute content in the best possible quality. In order to maintain a seamless playback, VoD applications commonly use a large reception buffer. However, in live streaming, the use of large buffers is not allowed because of the induced delay. Active Queue Management (AQM) arises as an alternative to control the congestion in router’s queue, pressing the traffic sources to reduce their transmission rate when it detects incipient congestion. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming. Furthermore, we propose a new AQM algorithm to improve the user-perceived video quality. The results show that the proposed method achieves better performance than competing AQM algorithms and improves the video quality in terms of average peak signal-to-noise ratio while keeping the fairness among concurrent flows.