Machine Learning based KPI Monitoring of Video Streaming Traffic for QoE Estimation
- 17 May 2021
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM SIGMETRICS Performance Evaluation Review
- Vol. 48 (4), 33-36
- https://doi.org/10.1145/3466826.3466839
Abstract
Quality of Experience (QoE) monitoring of video streaming traffic is crucial task for service providers. Nowadays it is challenging due to the increased usage of end-to-end encryption. In order to overcome this issue, machine learning (ML) approaches for QoE monitoring have gained popularity in the recent years. This work proposes a framework which includes a machine learning pipeline that can be used for detecting key QoE related events such as buffering events and video resolution changes for ongoing YouTube video streaming sessions in real-time. For this purpose, a ML model has been trained using YouTube streaming traffic collected from Android devices. Later on, the trained ML model is deployed in the framework's pipeline to make online predictions. The ML model uses statistical traffic information observed from the network-layer for learning and predicting the video QoE related events. It reaches 88% overall testing accuracy for predicting the video events. Although our work is yet at an early stage, the application of the ML model for online detection and prediction of video events yields quite promising results.Keywords
This publication has 6 references indexed in Scilit:
- Let me Decrypt your Beauty: Real-time Prediction of Video Resolution and Bitrate for Encrypted Video StreamingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2019
- Features that Matter: Feature Selection for On-line Stalling Prediction in Encrypted Video StreamingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2019
- Stream-based Machine Learning for Real-time QoE Analysis of Encrypted Video Streaming TrafficPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2019
- PhD thesisACM SIGMultimedia Records, 2018
- Impact of intermediate layer on quality of experience of HTTP adaptive streamingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Advanced MOS calculation for network based QoE Estimation of TCP streamed Video ServicesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013