EDQD: An Edge-Driven Multi-Agent DRL Solution for Improving Joint QoE in DASH-based Rich Media Content Delivery

Subhan, Fazal E., Yaqoob, Abid, Muntean, Cristina Hava and Muntean, Gabriel-Miro (2024) EDQD: An Edge-Driven Multi-Agent DRL Solution for Improving Joint QoE in DASH-based Rich Media Content Delivery. In: 2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, Toronto, ON, Canada, pp. 1-7. ISBN 979-8-3503-6426-2

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Abstract

In the realm of adaptive video streaming, ensuring high Quality of Experience (QoE) across multiple clients in heterogeneous network environments stands as a significant challenge. This paper delves into the augmentation of QoE fairness and adaptive bitrate aggregation in Dynamic Adaptive Streaming over HTTP (DASH), particularly tailored for multiple rich media clients. This study proposes an edge-driven MultiAgent Deep Reinforcement Learning (MADRL) solution for improving joint QoE in DASH-based rich media content delivery (EDQD) to orchestrate QoE-centric video streaming framework. In order to address the complexities of network conditions at the edge for optimal media streaming bitrate allocation the cooperative strategies among agents are of great importance. Therefore, the EDQD objective is to training agents in such a way to learn policies that optimize not only individual client QoE but also ensure fairness among clients to enhance the overall rich streaming experience. Here, in comparison with previously utilized Asynchronous Advantage Actor-Critic (A3C) algorithm, the Counterfactual Multi-Agent Policy Gradients (COMA) experimentation and training based on 5G network traces has the potential to demonstrate substantial improvements in joint QoE by efficiently allocating bitrate across multiple client in the edge-driven scenario. This research provides a major contribution to the emerging field of QoE-driven rich video streaming by highlighting the effectiveness of novel MADRL-based approach in edge-driven architectures that is designed to provide immersive and high-fidelity rich media experiences. In terms of average QoE, the experimental findings reported in this work outperform other state-of-the-art adaptive edge driven bitrate algorithms by 6.9% and 16.8%.

Item Type:	Book Section
Uncontrolled Keywords:	Training; Bit rate; Transform coding; Computer architecture; Streaming media; Media; Quality assessment; MPEG DASH; QoE Fairness; Bitrate adaptation; Edge Computing; Deep Reinforcement Learning
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Multimedia Communications H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management > Total Quality Management
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	20 Dec 2024 14:25
Last Modified:	20 Dec 2024 14:25
URI:	https://norma.ncirl.ie/id/eprint/7229

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