Developing a predictive model of quality of experience for internet video
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Developing a Predictive Model of Quality of Experience for Internet Video. Athula Balachandran , Vyas Sekarz , Aditya Akellay , Srinivasan Seshan , Ion Stoica , and Hui Zhang SIGCOMM 2013. Goal and Challenges. To develop a predictive model of user QoE in viewing Internet video

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Developing a Predictive Model of Quality of Experience for Internet Video

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Developing a predictive model of quality of experience for internet video

Developing a Predictive Model ofQuality of Experience for Internet Video

AthulaBalachandran, VyasSekarz, AdityaAkellay, SrinivasanSeshan, Ion Stoica, and Hui Zhang

SIGCOMM 2013


Goal and challenges

Goal and Challenges

  • To develop a predictive model of user QoE in viewing Internet video

  • Challenges:

    • Relationship between quality and engagement

    • Dependencies between quality metrics

    • Confounding factors


Dataset

Dataset

  • It was collected by conviva.com in real time using a client-side instrumentation library

  • 40 million video sessions collected over 3 months under two popular video content providers

  • One provider serves mostly VOD content, and another provider serves live broadcast for sports events.


Relationship between quality and engagement

Relationship between quality and engagement

  • Engagement linearly decreases with increasing rate of buffering up to 0.3 buff events/min

0.3


Confounding factors v s e ngagement

Confounding factors v.s. Engagement

  • Some factors may affect user viewing behavior and result in different observed engagements


Confounding factors

Confounding factors

  • Figure out effective cofounding factors by relative information gain


Confounding factors device

Confounding factors-Device

  • For a VOD subset dataset, increased bitrate led to lower engagement in the case of TV


Confounding factors device1

Confounding factors-Device

  • It shows that mobile users are more tolerant toward low quality


Quality model

Quality model

  • It computes the mean performance(buffering ratio, rate of buffering and join time) for each combination of attributes (e.g., type of video, ISP, region, device) and control parameters (e.g., bitrate and CDN) using empirical estimation


Predicted average engagement

Predicted Average Engagement


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