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Learning-Based Prediction, Rendering and Association Optimization for MEC-Enabled Wireless Virtual Reality (VR) Networks

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)6356-6370
Number of pages15
JournalIEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume20
Issue number10
DOIs
Accepted/In press2021
Published1 Oct 2021

Bibliographical note

Publisher Copyright: © 2002-2012 IEEE.

King's Authors

Abstract

Wireless-connected Virtual Reality (VR) provides immersive experience for VR users from anywhere at anytime. However, providing wireless VR users with seamless connectivity and real-time VR video with high quality is challenging due to its requirements in high Quality of Experience (QoE) and low VR interaction latency under limited computation capability of VR device. To address these issues, we propose a MEC-enabled wireless VR network, where the field of view (FoV) of each VR user can be real-time predicted using Recurrent Neural Network (RNN), and the rendering of VR content is moved from VR device to MEC server with rendering model migration capability. Taking into account the geographical and FoV request correlation, we propose centralized and distributed decoupled Deep Reinforcement Learning (DRL) strategies to maximize the long-term QoE of VR users under the VR interaction latency constraint. Simulation results show that our proposed MEC rendering schemes and DRL algorithms substantially improve the long-term QoE of VR users and reduce the VR interaction latency compared to rendering at VR devices.

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