Recommending Changes on QoE Factors with Conditional Variational AutoEncoder

Abstract

Increasing complexity in management of immense number of network elements and their dynamically changing environment necessitates machine learning based recommendation models to guide human experts in setting appropriate network configurations to sustain end-user Quality of Experience (QoE). In this paper, we present and demonstrate a generative Conditional Variational AutoEncoder (CVAE)-based technique to reconstruct realistic underlying QoE factors together with improvement suggestions in a video streaming use case. Based on our experiment setting consisting of a set of what-if scenarios, our approach pinpointed the potential required changes on the QoE factors to improve the estimated video Mean Opinion Scores (MOS).