Abstract

This paper presents Adaptive VR (AVIRA), a scheme that implements a Virtual Reality (VR) content-aware prioritisation transport to extend Multipath TCP (MPTCP) functionalities and improve its performance. To do so, AVIRA monitors the subflows operation and forecasts subflows’ performance by applying an Machine Learning (ML) approach to evaluate a set of features - such as latency and throughput - for every subflow available. This ML approach forecasts the performance of these features through linear regression and applies a linear classifier by using a weighted sum on the forecast results. When the traffic of a specific VR component is detected, AVIRA performs its prioritisation scheme by redirecting packets to the subflow with the best set of forecasted features. AVIRA outperforms the algorithms used for comparison and shows that the use of an ML approach in a "low-level" application is viable, especially in situations where the network features under scrutiny are subject to higher variations. In these scenarios, the AVIRA scheme can be outstandingly efficient.