New Publication @ SSP2021: Machine Learning Approach to Estimate Video QoE of Encrypted DASH Traffic in 5G Networks

R. Ul Mustafa, D. Moura, and C. E. Rothenberg, “Machine Learning Approach to Estimate Video QoE of Encrypted DASH Traffic in 5G Networks,” in In IEEE Singal Processing (SSP2021) Workshop, 2021.

Abstract:

5G communication technologies promise reduced latency and increased throughput, among other features. The so-called enhanced Mobile Broadband (eMBB) type of services will contribute to further adoption of video streaming services. In this work, we use a realistic emulation environment based on 5G traces to investigate how Dynamic Adaptive Streaming over HTTP (DASH) video content using three state-of-art Adaptive Bitrate Streaming (ABS) algorithms is impacted in static and mobility scenarios. Given the wide adoption of end-to-end encryption, we use machine learning (ML) models to estimate multiple key video Quality of Experience (QoE) indicators (KQIs) taking network-level Quality of Service (QoS) metrics as input features. The proposed feature extraction method does not require chunk-detection, significantly reducing the complexity of the monitoring approach and providing new means for QoE evaluation of HAS protocols. We show that our ML classifiers achieve a QoE prediction accuracy above 91%.
Published in:  IEEE Singal Processing (SSP2021) Workshop
Date of Publication: 7 November 2021
@INPROCEEDINGS{9513804,
  author={Ul Mustafa, Raza and Moura, David and Rothenberg, Christian Esteve},
  booktitle={2021 IEEE Statistical Signal Processing Workshop (SSP)}, 
  title={Machine Learning Approach to Estimate Video QoE of Encrypted DASH Traffic in 5G Networks}, 
  year={2021},
  volume={},
  number={},
  pages={586-589},
  doi={10.1109/SSP49050.2021.9513804}}
Please follow and like us:

About the Author

You may also like these