Temporal Characterization of VR Traffic for Network Slicing Requirement Definition

TL;DR

This paper analyzes VR traffic patterns to inform network slicing, using real data and prediction models to optimize resource allocation and ensure QoS in multi-party VR applications.

Contribution

It provides the first detailed temporal analysis of VR traffic in a real setup and introduces prediction models for dynamic resource management.

Findings

VR traffic exhibits significant fluctuations even in CBR mode.

Prediction models can accurately forecast future frame sizes.

Dynamic SLA adjustment improves QoS and resource efficiency.

Abstract

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.