journal article·2025-04-10Open access

Multiuser Access Control for 360° VR Video Service Systems Exploiting Proactive Caching and Mobile Edge Computing

Qiyan Weng, Yijing Tang, Hangguan Shan

Applied Sciences· Volume 15· Issue 8

Abstract

Mobile virtual reality (VR) is considered a killer application for future mobile broadband networks. However, for cloud VR, the long content delivery path and time-varying transmission rate from the content provider’s cloud VR server to the users make the quality-of-service (QoS) provisioning for VR users very challenging. To this end, in this paper, we design a 360° VR video service system that leverages proactive caching and mobile edge computing (MEC) technologies. Furthermore, we propose a multiuser access control algorithm tailored to the system, based on analytical results of the delay violation probability, which is derived considering the impact of both the multi-hop wired network from the cloud VR server to the MEC server and the wireless network from the MEC server-connected base station (BS) to the users. The proposed access control algorithm aims to maximize the number of served users by exploiting real-time and dynamic network resources, while ensuring that the end-to-end delay violation probability for each accessed user remains within an acceptable limit. Simulation results are presented to analyze the impact of diverse system parameters on both the user access probability and the delay violation probability of the accessed users, demonstrating the effectiveness of the proposed multiuser access control algorithm. It is observed in the simulation that increasing the computing capacity of the MEC server or the communication bandwidth of the BS is one of the most effective methods to accommodate more users for the system. In the tested scenarios, when the MEC server’s computing capacity (the BS’s bandwidth) increases from 0.8 Tbps (50 MHz) to 3.2 Tbps (150 MHz), the user access probability improves on average by 92.53% (85.49%).
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Caching and Content Delivery · Advanced Wireless Network Optimization · Opportunistic and Delay-Tolerant Networks

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Funding

Natural Science Foundation of China · Zhejiang Provincial Natural Science Foundation of China · Science and Technology Development Fund · State Key Laboratory of Internet of Things for Smart City (University of Macau)

References (24)

Show source-provided references
1. Bahrami, M. (April, January 30). Cloud computing for emerging mobile cloud Apps. Proceedings of the IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, San Francisco, CA, USA.
2. Virtual reality laboratories in engineering blended learning environments: Challenges and opportunities
3. A survey on adaptive 360 video streaming: Solutions, challenges and opportunities10.1109/COMST.2020.3006999 ↗
4. Ebublio: Edge-assisted multiuser 360° video streaming10.1109/JIOT.2023.3263885 ↗
5. Tharakan, K.S., Dahrouj, H., Kouzayha, N., Elsawy, H., and Al-Naffouri, T.Y. (2025). Personalized federated learning for cellular VR: Online learning and dynamic caching. IEEE Trans. Commun.10.1109/TCOMM.2025.3535878 ↗
6. Low-latency VR video processing-transmitting system based on edge computing10.1109/TBC.2024.3380455 ↗
7. Multi-cluster cooperative offloading for VR task: A MARL approach with graph embedding10.1109/TMC.2024.3354056 ↗
8. Qian, F., Ji, L., Han, B., and Gopalakrishnan, V. (2016, January 3–7). Optimizing 360° video delivery over cellular networks. Proceedings of the 5th Workshop on All Things Cellular: Operations, Applications and Challenges, New York, NY, USA.10.1145/2980055.2980056 ↗
9. Cloud-edge-end collaborative caching based on graph learning for cyber-physical virtual reality10.1109/JSYST.2023.3262255 ↗
10. Cooperative caching, rendering, and beamforming for RIS-assisted wireless virtual reality networks10.1109/TVT.2023.3345354 ↗
11. Joint radio communication, caching, and computing design for mobile virtual reality delivery in fog radio access networks10.1109/JSAC.2019.2916486 ↗
12. Communications, caching, and computing for mobile virtual reality: Modeling and tradeoff10.1109/TCOMM.2019.2920594 ↗
13. Design and analysis of MEC-and proactive caching-based 360° mobile VR video streaming10.1109/TMM.2021.3067205 ↗
14. Rendering-aware VR video caching over multi-cell MEC networks10.1109/TVT.2021.3057684 ↗
15. A cooperative caching and computing-offloading method for 3C trade-off in VR video services10.1109/ACCESS.2021.3110741 ↗
16. Decentralized cooperative caching and offloading for virtual reality task based on GAN-powered multi-agent reinforcement learning10.1109/TSC.2023.3347741 ↗
17. Bazzani, L. (2017, January 24–26). Recurrent mixture density network for spatiotemporal visual attention. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France.
18. Predicting head movement in panoramic video: A deep reinforcement learning approach10.1109/TPAMI.2018.2858783 ↗
19. Impact of network dynamics on user’s video quality: Analytical framework and QoS provision10.1109/TMM.2009.2036294 ↗
20. MEC-assisted immersive VR video streaming over Terahertz wireless networks: A deep reinforcement learning approach10.1109/JIOT.2020.3003449 ↗
21. Cost-aware task offloading and migration for wireless virtual reality using interactive A3C approach10.1109/TVT.2024.3374303 ↗
22. Scalable 360° video stream delivery: Challenges, solutions, and opportunities10.1109/JPROC.2019.2894817 ↗
23. An optimal tile-based approach for viewport-adaptive 360-degree video streaming10.1109/JETCAS.2019.2899488 ↗
24. (2025, February 15). Cloud VR Solution Whitepaper. Available online: https://www-file.huawei.com/-/media/corporate/pdf/ilab/2018/cloud_vr_solutions_wp_cn.pdf?source=corp_comm.