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

Learning-based Prediction and Uplink Retransmission for Wireless Virtual Reality (VR) Network