  Cloud virtual reality (VR) gaming traffic characteristics such as frame size,
inter-arrival time, and latency need to be carefully studied as a first step
toward scalable VR cloud service provisioning. To this end, in this paper we
analyze the behavior of VR gaming traffic and Quality of Service (QoS) when VR
rendering is conducted remotely in the cloud. We first build a VR testbed
utilizing a cloud server, a commercial VR headset, and an off-the-shelf WiFi
router. Using this testbed, we collect and process cloud VR gaming traffic data
from different games under a number of network conditions and fixed and
adaptive video encoding schemes. To analyze the application-level
characteristics such as video frame size, frame inter-arrival time, frame loss
and frame latency, we develop an interval threshold based identification method
for video frames. Based on the frame identification results, we present two
statistical models that capture the behaviour of the VR gaming video traffic.
The models can be used by researchers and practitioners to generate VR traffic
models for simulations and experiments - and are paramount in designing
advanced radio resource management (RRM) and network optimization for cloud VR
gaming services. To the best of the authors' knowledge, this is the first
measurement study and analysis conducted using a commercial cloud VR gaming
platform, and under both fixed and adaptive bitrate streaming. We make our VR
traffic data-sets publicly available for further research by the community.
