Intelligent QoE Management for IoMT Streaming Services in Multi-User Downlink RSMA Networks
- Authors
- Nguyen, The-Vinh; Hua, Duc-Thien; Huong, Thien Ho; Hoang, Vinh Truong; Dao, Nhu-Ngoc; Cho, Sungrae
- Issue Date
- Apr-2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Downlink; Internet of multimedia things; mobile network; NOMA; Quality of experience; quality of experience; rate splitting multiple access; Resource management; Solid modeling; Streaming media; Wireless communication
- Citation
- IEEE Internet of Things Journal, v.11, no.7, pp 12602 - 12618
- Pages
- 17
- Journal Title
- IEEE Internet of Things Journal
- Volume
- 11
- Number
- 7
- Start Page
- 12602
- End Page
- 12618
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/70746
- DOI
- 10.1109/JIOT.2023.3334473
- ISSN
- 2327-4662
- Abstract
- The exponential growth of the Internet of Multimedia Things (IoMT) traffic has posed a threat of service quality degradation due to the limitation of current communication, networking, and computing advances in mobile networks. In this regard, managing the Quality-of-Experience (QoE) for IoMT services is a vital challenge to meet user satisfaction. To cope with this problem, we investigate the joint optimization of video quality variation and latency in multi-user downlink Rate-Splitting Multiple-Access (RSMA) networks, especially within imperfect network conditions and state information. To accomplish this, we first formulated the joint optimization problem into a Markov decision process framework, then exploited a deep reinforcement learning approach to adaptively calculate the optimal configuration of the RSMA against environment dynamics. As a result, the proposed Deep Deterministic Policy Gradient on RSMA-based Video streaming System (DDPG-RMAVS) provides QoE maintenance by minimizing video resolution reduction and latency. Extensive simulation results revealed that the proposed DDPG-RMAVS algorithm surpasses existing algorithms by achieving higher video quality, lower delay, larger buffer capacity, and limited stalling events, representing a significant breakthrough in IoMT streaming optimization. IEEE
- Files in This Item
-
- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.