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Full-immersive multiuser Virtual Reality (VR) envisions supportingunconstrained mobility of the users in the virtual worlds, while at the sametime constraining their physical movements inside VR setups through redirectedwalking. For enabling delivery of high data rate video content in real-time,the supporting wireless networks will leverage highly directional communicationlinks that will "track" the users for maintaining the Line-of-Sight (LoS)connectivity. Recurrent Neural Networks (RNNs) and in particular LongShort-Term Memory (LSTM) networks have historically presented themselves as asuitable candidate for near-term movement trajectory prediction for naturalhuman mobility, and have also recently been shown as applicable in predictingVR 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 thetraditionally utilized LSTMs. Second, we show that context from a virtual worldcan enhance the accuracy of the prediction if used as an additional inputfeature in comparison to the more traditional utilization of solely thehistorical physical movements of the VR users. Finally, we show that theprediction system trained on a static number of coexisting VR users be scaledto a multi-user system without significant accuracy degradation.

Short-Term Trajectory Prediction for Full-Immersive Multiuser Virtual Reality with Redirected Walking