research-article

I See What you See: Real Time Prediction of Video Quality from Encrypted Streaming Traffic

Authors:

Sarah Wassermann,

Michael Seufert,

Kuang LiAuthors Info & Claims

Internet-QoE'19: Proceedings of the 4th Internet-QoE Workshop on QoE-based Analysis and Management of Data Communication Networks

Pages 1 - 6

https://doi.org/10.1145/3349611.3355549

Published: 04 October 2019 Publication History

Abstract

We address the problem of real-time QoE monitoring of HAS, from the ISP perspective, focusing in particular on video-resolution analysis. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to predict different video resolution levels in a fine-grained scale, ranging from 144p to 1080p resolution, using as input only packet-level data. The proposed measurement system performs predictions in real time, during the course of an ongoing video-streaming session, with a time granularity as small as one second. We consider the particular case of YouTube video streaming. Empirical evaluations on a large and heterogeneous corpus of YouTube measurements demonstrate that the proposed system can predict video resolution with very high accuracy, and in real time. Different from state of the art, the prediction task is not bound to coarse-grained video quality classes and does not require chunk-detection approaches for feature extraction.

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Cited By

Seufert MOrsolic I(2024)Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2023.332666421:3(2824-2836)Online publication date: Jun-2024
https://doi.org/10.1109/TNSM.2023.3326664
Skaka ‐ Čekić FBaraković Husić J(2023)A feature selection for video quality of experience modeling: A systematic literature reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.149713:3Online publication date: 3-Apr-2023
https://doi.org/10.1002/widm.1497
Wu HLi XWang GCheng GHu X(2022)Resolution Identification of Encrypted Video Streaming Based on HTTP/2 FeaturesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/355189119:2(1-23)Online publication date: 28-Jul-2022
https://dl.acm.org/doi/10.1145/3551891
Show More Cited By

Index Terms

I See What you See: Real Time Prediction of Video Quality from Encrypted Streaming Traffic
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Networks
  1. Network performance evaluation
    1. Network measurement
    2. Network performance analysis
  2. Network services
    1. Network monitoring

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Published In

cover image ACM Conferences

Internet-QoE'19: Proceedings of the 4th Internet-QoE Workshop on QoE-based Analysis and Management of Data Communication Networks

October 2019

50 pages

ISBN:9781450369275

DOI:10.1145/3349611

General Chairs:
Pedro Casas
Austrian Institute of Technology, Austria
,
Florian Wamser
University of Würzburg, Germany
,
Fabian Bustamante
Northwestern University, USA
,
David Choffnes
Northeastern University, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 October 2019

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MobiCom '19

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SIGMOBILE

MobiCom '19: The 25th Annual International Conference on Mobile Computing and Networking

October 21, 2019

Los Cabos, Mexico

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Overall Acceptance Rate 10 of 21 submissions, 48%

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Cited By

Seufert MOrsolic I(2024)Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2023.332666421:3(2824-2836)Online publication date: Jun-2024
https://doi.org/10.1109/TNSM.2023.3326664
Skaka ‐ Čekić FBaraković Husić J(2023)A feature selection for video quality of experience modeling: A systematic literature reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.149713:3Online publication date: 3-Apr-2023
https://doi.org/10.1002/widm.1497
Wu HLi XWang GCheng GHu X(2022)Resolution Identification of Encrypted Video Streaming Based on HTTP/2 FeaturesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/355189119:2(1-23)Online publication date: 28-Jul-2022
https://dl.acm.org/doi/10.1145/3551891
Izima Ode Fréin RMalik A(2021)A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery MetricsElectronics10.3390/electronics1022285110:22(2851)Online publication date: 19-Nov-2021
https://doi.org/10.3390/electronics10222851
Carofiglio GGrassi GLoparco EMuscariello LPapalini MSamain J(2021)Characterizing the Relationship Between Application QoE and Network QoS for Real-Time ServicesProceedings of the ACM SIGCOMM 2021 Workshop on Network-Application Integration10.1145/3472727.3472800(20-25)Online publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1145/3472727.3472800
Kattadige CChoi KWijesinghe ANama AThilakarathna KSeneviratne SJourjon G(2021)SETA++: Real-Time Scalable Encrypted Traffic Analytics in Multi-Gbps NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2021.308509718:3(3244-3259)Online publication date: Sep-2021
https://doi.org/10.1109/TNSM.2021.3085097
Lange SSchwarzmann SGajić MZinner TKraemer F(2021)AI in 5G Networks: Challenges and Use CasesCommunication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning10.1002/9781119675525.ch5(101-122)Online publication date: 3-Sep-2021
https://doi.org/10.1002/9781119675525.ch5
Gutterman CGuo KArora SGilliland TWang XWu LKatz-Bassett EZussman G(2020)RequetACM Transactions on Multimedia Computing, Communications, and Applications10.1145/339449816:2s(1-28)Online publication date: 10-Jul-2020
https://dl.acm.org/doi/10.1145/3394498
Wassermann SSeufert MCasas PGang LLi K(2020)ViCrypt to the Rescue: Real-Time, Machine-Learning-Driven Video-QoE Monitoring for Encrypted Streaming TrafficIEEE Transactions on Network and Service Management10.1109/TNSM.2020.303649717:4(2007-2023)Online publication date: Dec-2020
https://doi.org/10.1109/TNSM.2020.3036497
Schwarzmann SMarquezan CTrivisonno RNakajima SZinner T(2020)Accuracy vs. Cost Trade-off for Machine Learning Based QoE Estimation in 5G NetworksICC 2020 - 2020 IEEE International Conference on Communications (ICC)10.1109/ICC40277.2020.9148685(1-6)Online publication date: Jun-2020
https://doi.org/10.1109/ICC40277.2020.9148685
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