King's College London

Research portal

Learning-based Prediction and Proactive Uplink Retransmission for Wireless Virtual Reality Network

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)10723-10734
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Volume70
Issue number10
DOIs
Accepted/In press2021
Published1 Oct 2021

Bibliographical note

Publisher Copyright: © 1967-2012 IEEE.

King's Authors

Abstract

Wireless Virtual Reality (VR) users are able to enjoy immersive experience from anywhere at anytime. However, providing full spherical VR video with high quality under limited VR interaction latency is challenging. If the viewpoint of the VR user can be predicted in advance, only the required viewpoint is needed to be rendered and delivered, which can reduce the VR interaction latency. Therefore, in this paper, we use offline and online learning algorithms to predict viewpoint of the VR user using real VR dataset. For the offline learning algorithm, the trained learning model is directly used to predict the viewpoint of VR users in continuous time slots. While for the online learning algorithm, based on the VR user's actual viewpoint delivered through uplink transmission, we compare it with the predicted viewpoint and update the parameters and input viewpoints of the online learning algorithm to further improve the prediction accuracy. To guarantee the reliability of the uplink transmission, we integrate the Proactive retransmission scheme into our proposed online learning algorithm. Simulation results show that our proposed online learning algorithm for uplink wireless VR network with the proactive retransmission scheme only exhibits about 5% prediction error.

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454