A MAP-based Performance Analysis on 5G-powered Cloud VR Streaming

Despite that cloud virtual reality (VR) is the most promising service in 5G networks, a reasonable traffic model for it is still unknown. Based on statistics of real cloud VR traces, we justify that the Markovian arrival process (MAP) can well characterize the inter-arrival times (IATs) among packets. Moreover, we discuss possible methods to efficiently estimate parameters for flow aggregation. Since MAPs are analytically tractable, we quantify the performance of delivering cloud VR traffic from a queueing theory perspective. Aiming at supporting quantile metrics, we provide distributions of queue-length and latency for an arbitrary packet. The accuracy of estimated latency is validated by comparing with measured latency on an industrial 5G platform. The MAP-based traffic model will potentially serve as an input for performance evaluation and network planning for assuring high requirements of user experience.