Work

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

Year: 2023

Type: article

Abstract: Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in video transmission across networks. Traditional adaptive bitrate (ABR) algorithms adjust the quality of video segments based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules within a complex environment, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel Deep Reinforcement Learning-based approach for streaming DASH, focusing on maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes the Quality of Experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate this approach through a simulation model that encompasses diverse wireless network environments and various video sequences. Additionally, we compare our proposed approach with state-of-the-art methods. The experimental results demonstrate significant improvements in QoE, ensuring users enjoy stable, high-quality video streaming sessions.

Source: Applied Sciences

DOI: 10.3390/app132111697

Authors: Naima Souane, Malika Bourenane, Yassine Douga

Institutions: Université Oran 1 Ahmed Ben Bella, University of Blida

Language: English

Topic: Image and Video Quality Assessment

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,558

Funder: Direction Générale de la Recherche Scientifique et du Développement Technologique