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Pregled bibliografske jedinice broj: 870774

A Machine Learning Approach to Classifying YouTube QoE Based on Encrypted Network Traffic


Oršolić, Irena; Pevec, Dario; Sužnjević, Mirko; Skorin-Kapov, Lea
A Machine Learning Approach to Classifying YouTube QoE Based on Encrypted Network Traffic // Multimedia tools and applications, 76 (2017), 21; 22267-22301 doi:10.1007/s11042-017-4728-4 (međunarodna recenzija, članak, znanstveni)


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Naslov
A Machine Learning Approach to Classifying YouTube QoE Based on Encrypted Network Traffic

Autori
Oršolić, Irena ; Pevec, Dario ; Sužnjević, Mirko ; Skorin-Kapov, Lea

Izvornik
Multimedia tools and applications (1380-7501) 76 (2017), 21; 22267-22301

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Quality of Experience ; Video Streaming ; HTTP adaptive streaming ; YouTube ; network measurements ; passive monitoring ; machine learning

Sažetak
Due to the widespread use of encryption in Over-The-Top video streaming traffic, network operators generally lack insight into application-level quality indicators (e.g., video quality levels, buffer underruns, stalling duration). They are thus faced with the challenge of finding solutions for monitoring service performance and estimating customer Quality of Experience (QoE) degradations based solely on passive monitoring solutions deployed within their network. We address this challenge by considering the concrete case of YouTube, whereby we present a methodology for the classification of end users' QoE when watching YouTube videos, based only on statistical properties of encrypted network traffic. We have developed a system called YouQ which includes tools for monitoring and analysis of application-level quality indicators and corresponding traffic traces. Collected data is then used for the development of machine learning models for QoE classification based on computed traffic features per video session. To test the YouQ system and methodology, we collected a dataset corresponding to 1060 different YouTube videos streamed across 39 different bandwidth scenarios, and tested various classification models. Classification accuracy was found to be up to 84% when using three QoE classes ("low", "medium" or "high") and up to 91% when using binary classification (classes "low" and "high"). To improve the models in the future, we discuss why and when prediction errors occur. Moreover, we have analysed YouTube's adaptation algorithm, thus providing valuable insight into the logic behind the quality level selection strategy, which may also be of interest in improving future QoE estimation algorithms.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ-UIP-2014-09-5605 - Kooperativno upravljanje iskustvenom kvalitetom upokretnim mrežama za interaktivne višemedijske aplikacije u računalom oblaku (Q-MANIC) (Lea Skorin-Kapov, HRZZ)

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Lea Skorin-Kapov (autor)

Avatar Url Irena Oršolić (autor)

Avatar Url Dario Pevec (autor)

Citiraj ovu publikaciju

Oršolić, Irena; Pevec, Dario; Sužnjević, Mirko; Skorin-Kapov, Lea
A Machine Learning Approach to Classifying YouTube QoE Based on Encrypted Network Traffic // Multimedia tools and applications, 76 (2017), 21; 22267-22301 doi:10.1007/s11042-017-4728-4 (međunarodna recenzija, članak, znanstveni)
Oršolić, I., Pevec, D., Sužnjević, M. & Skorin-Kapov, L. (2017) A Machine Learning Approach to Classifying YouTube QoE Based on Encrypted Network Traffic. Multimedia tools and applications, 76 (21), 22267-22301 doi:10.1007/s11042-017-4728-4.
@article{article, year = {2017}, pages = {22267-22301}, DOI = {10.1007/s11042-017-4728-4}, keywords = {Quality of Experience, Video Streaming, HTTP adaptive streaming, YouTube, network measurements, passive monitoring, machine learning}, journal = {Multimedia tools and applications}, doi = {10.1007/s11042-017-4728-4}, volume = {76}, number = {21}, issn = {1380-7501}, title = {A Machine Learning Approach to Classifying YouTube QoE Based on Encrypted Network Traffic}, keyword = {Quality of Experience, Video Streaming, HTTP adaptive streaming, YouTube, network measurements, passive monitoring, machine learning} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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