Deep Reinforcement Learning-Based Approach for Video Streaming: Dynamic Adaptive Video Streaming over HTTP

Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in delivering video across networks. Traditional adaptive bitrate (ABR) algorithms adjust video segment quality based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules, making it challengin...

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Veröffentlicht in:Applied sciences Jg. 13; H. 21; S. 11697
Hauptverfasser: Souane, Naima, Bourenane, Malika, Douga, Yassine
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.11.2023
Schlagworte:
ABR
Adaptation
Algorithms
Artificial intelligence
Bandwidths
DASH
Decision making
Deep learning
Neural networks
QoE
Streaming media
video streaming
Wireless networks
Norway
ISSN:2076-3417, 2076-3417
Online-Zugang:Volltext
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Zusammenfassung:Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in delivering video across networks. Traditional adaptive bitrate (ABR) algorithms adjust video segment quality based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel deep-reinforcement-learning-based approach for DASH streaming, with the primary focus of maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes quality of experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate our approach through a comprehensive simulation model encompassing diverse wireless network environments and various video sequences. We also conduct a comparative analysis with state-of-the-art methods. The experimental results demonstrate significant improvements in QoE, ensuring users enjoy stable, high-quality video streaming sessions.
Bibliographie:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app132111697