  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 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.
