  Over the past few years, the concept of VR has attracted increasing interest
thanks to its extensive industrial and commercial applications. Currently, the
3D models of the virtual scenes are generally stored in the VR visor itself,
which operates as a standalone device. However, applications that entail
multi-party interactions will likely require the scene to be processed by an
external server and then streamed to the visors. However, the stringent Quality
of Service (QoS) constraints imposed by VR's interactive nature require Network
Slicing (NS) solutions, for which profiling the traffic generated by the VR
application is crucial. To this end, we collected more than 4 hours of traces
in a real setup and analyzed their temporal correlation. More specifically, we
focused on the CBR encoding mode, which should generate more predictable
traffic streams. From the collected data, we then distilled two prediction
models for future frame size, which can be instrumental in the design of
dynamic resource allocation algorithms. Our results show that even the
state-of-the-art H.264 CBR mode can have significant fluctuations, which can
impact the NS optimization. We then exploited the proposed models to
dynamically determine the Service Level Agreement (SLA) parameters in an NS
scenario, providing service with the required QoS while minimizing resource
usage.
