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A machine learning approach to classifying YouTube QoE based on encrypted network traffic

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Abstract

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.

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Acknowledgements

This work has been conducted in the scope of the project “Survey and analysis of monitoring solutions for YouTube network traffic and application layer KPIs” funded by Ericsson Nikola Tesla, Croatia. This work has also been supported in part by the Croatian Science Foundation under the project UIP-2014-09-5605 (Q-MANIC).

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000, Zagreb, Croatia

    Irena Orsolic, Dario Pevec, Mirko Suznjevic & Lea Skorin-Kapov

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  1. Irena Orsolic
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  2. Dario Pevec
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  3. Mirko Suznjevic
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  4. Lea Skorin-Kapov
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Corresponding author

Correspondence to Irena Orsolic.

Appendix A: Network traffic features

Appendix A: Network traffic features

The table below explains all the extracted features. The features are calculated for the network traffic captured during the watch time of each video.

Feature name

Feature description

avgPacketSize

Average packet size [b y t e s]

averageSizeThroughTime

Average of sizes of transferred data per 5s interval [b y t e s]

minimalSizeThroughTime

Minimum of sizes of transferred data per 5s interval [b y t e s]

maximalSizeThroughTime

Maximum of sizes of transferred data per 5s interval [b y t e s]

sizeThroughTimeStdDev

Standard deviation of sizes of transferred data per 5s interval

 

[b y t e s]

sizeThroughTimeMedian

Median of sizes of transferred data per 5s interval [b y t e s]

averageInterarrivalTime

Average packet interarrival time [s]

minimalInterarrivalTime

Minimal packet interarrival time [s]

maximalInterarrivalTime

Maximal packet interarrival time [s]

avgInterarrivalTimeThroughTime

Average of interarrival time averages per 5s interval [s]

interarrivalTimeThroughTimeStdDev

Standard deviation of interarrival time averages per 5s

 

interval [s]

interarrivalTimeThroughTimeMedian

Median of interarrival time averages per 5s interval [s]

effectiveThroughput

Average of average throughput values calculated per 5s

 

intervals, including only those intervals where throughput

 

per interval was higher than 0.3 Mbps [M b p s]

minThroughputThroughTime

Minimum of average throughputs per 5s interval [M b p s]

maxThroughputThroughTime

Maximum of average throughputs per 5s interval [M b p s]

throughputStdDev

Standard deviation of average throughputs per 5s interval

 

[M b p s]

throughputMedian

Median of average throughputs per 5s interval [M b p s]

initialThroughput2

Throughput in first 2 seconds [M b p s]

initialThroughput3

Throughput in first 3 seconds [M b p s]

initialThroughput5

Throughput in first 5 seconds [M b p s]

initialThroughput10

Throughput in first 10 seconds [M b p s]

dupack

Number of duplicate acknowledgements

dupackOverAll

Ratio of duplicate acknowledgements

retransmission

Number of retransmissions

retransmissionOverAll

Retransmission ratio

ackLostSegment

Number of packets that acknowledge lost segment

ackLostSegmentOverAll

Ratio of packets that acknowledge lost segment

push

Number of packets with TCP flag push set

pushOverAll

Ratio of packets with TCP flag push set

reset

Number of packets with TCP flag reset set

resetOverAll

Ratio of packets with TCP flag reset set

numberOfFlows

Number of TCP flows established

numberOfServers

Number of contacted servers

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Orsolic, I., Pevec, D., Suznjevic, M. et al. A machine learning approach to classifying YouTube QoE based on encrypted network traffic. Multimed Tools Appl 76, 22267–22301 (2017). https://doi.org/10.1007/s11042-017-4728-4

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