Functional Linear Regression for the prediction of streaming video QoE

Streaming video is an ubiquitous service used by billions of people every day. Offering the best-in-class Quality of Experience (QoE) is a challenge to Mobile Network Operators (MNOs) which requires them to identify the obstacles to overcome. To properly achieve this, MNOs require efficient monitoring able to maintain this QoE at optimum levels. Machine Learning (ML) models play an essential role in this task. This paper introduces PenFFR, a novel approach based on Functional Data Analysis (FDA), to identify the underlying functions between the QoE and the features that characterize the streaming videos thus enabling to predict future values. The experimental results show that the performance of the prediction is on par for one metric with Deep Learning-based methods and even better for another metric. To facilitate the understanding of our method and enable hands-on application, the code is made available [1].