Transfer Learning for Online Prediction of Virtual Reality Cloud Gaming Traffic

Cloud-based Virtual Reality (VR) gaming is gaining popularity to provide immersive experiences without requiring bulky hardware. However, managing network resources for these games is crucial to prevent subpar user experience and unnecessary costs. Predicting VR traffic patterns, such as video frame sizes can enable proactive network resource allocation and lead to improved quality of service (QoS). To this end, in this paper we first evaluate the efficacy of various Machine Learning (ML) models for predicting gaming traffic frame sizes using data collected from a real-world cloud-based VR game testbed. We then investigate the effectiveness of transfer learning (TL) in predicting frame size traffic patterns across different games and network conditions using an online learning method that we propose. The findings show that using the TL approach for online learning prediction can reduce overall traffic prediction error by up to 54%. Overall, this paper contributes to the understanding of cloud-based VR traffic patterns and can be of interest to developers, practitioners, and researchers interested in optimizing the performance of such systems.