  Full-immersive multiuser Virtual Reality (VR) envisions supporting
unconstrained mobility of the users in the virtual worlds, while at the same
time constraining their physical movements inside VR setups through redirected
walking. For enabling delivery of high data rate video content in real-time,
the supporting wireless networks will leverage highly directional communication
links that will "track" the users for maintaining the Line-of-Sight (LoS)
connectivity. Recurrent Neural Networks (RNNs) and in particular Long
Short-Term Memory (LSTM) networks have historically presented themselves as a
suitable candidate for near-term movement trajectory prediction for natural
human mobility, and have also recently been shown as applicable in predicting
VR users' mobility under the constraints of redirected walking. In this work,
we extend these initial findings by showing that Gated Recurrent Unit (GRU)
networks, another candidate from the RNN family, generally outperform the
traditionally utilized LSTMs. Second, we show that context from a virtual world
can enhance the accuracy of the prediction if used as an additional input
feature in comparison to the more traditional utilization of solely the
historical physical movements of the VR users. Finally, we show that the
prediction system trained on a static number of coexisting VR users be scaled
to a multi-user system without significant accuracy degradation.
