Reinforcement learning-based rate adaptation in dynamic video streaming

N. A. Hafez (), M. S. Hassan () and T. Landolsi ()
Additional contact information
N. A. Hafez: American University of Sharjah
M. S. Hassan: American University of Sharjah
T. Landolsi: American University of Sharjah

Telecommunication Systems: Modelling, Analysis, Design and Management, 2023, vol. 83, issue 4, No 6, 395-407

Abstract: Abstract Video streaming stands out as the most significant traffic type consumed by mobile devices. This increased demand has been a major driver for research on bitrate adaptation algorithms. Bitrate adaptation ensures high user-perceived quality, which, in turn, correlates with higher profits for content providers and delivery systems. Dynamic adaptive streaming over HTTP (DASH) is a widely adopted video streaming standard utilized by service providers to provide competitive quality of experience (QoE). It is capable of providing seamless streaming via uncertain network conditions by switching across different video qualities and their corresponding video segment bitrates. The complexity of the video streaming environment makes it a good candidate for different learning-based approaches. Accordingly, this paper proposes a reinforcement-learning (RL) deep Q-network called DQNReg, that enhances the classical deep Q-learning method. A segment-wise QoE-based reward function is formulated so that the learning strategy can converge towards maximizing the QoE outcome. The proposed RL-based adaptation approach is evaluated using trace-based simulation for both wireless local area network channels and 5G mobile channels. The performance of this RL-based method is compared to three methods: A heuristic method, a model-based method, and a classical learning-based method. The comparison shows that the RL-based method converges faster while achieving a high QoE score. In addition, it reduces the re-buffering duration while maintaining higher video quality and relatively lower quality variations.

Keywords: Bitrate adaptation; DASH; Deep Q-learning; Optimization; Reinforcement learning; Video streaming (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11235-023-01031-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:telsys:v:83:y:2023:i:4:d:10.1007_s11235-023-01031-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/11235

DOI: 10.1007/s11235-023-01031-3

Access Statistics for this article

Telecommunication Systems: Modelling, Analysis, Design and Management is currently edited by Muhammad Khan

More articles in Telecommunication Systems: Modelling, Analysis, Design and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().