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Understanding the Impact of Video Quality on User Engagement

SIGCOMM 2011. Understanding the Impact of Video Quality on User Engagement. Outline. Introduction Datasets and Metrics Analysis Techniques Engagement View Level Viewer Level Lessons Conclusion. Introduction. Internet video has become more and more popular What impacts engagement?!

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Understanding the Impact of Video Quality on User Engagement

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  1. SIGCOMM 2011 Understanding the Impact of Video Quality on User Engagement

  2. Outline • Introduction • Datasets and Metrics • Analysis Techniques • Engagement • View Level • Viewer Level • Lessons • Conclusion

  3. Introduction • Internet video has become more and more popular • What impacts engagement?! • Not well understood yet

  4. Introduction • Given the same video, does Quality impact Engagement?! • What are the most critical metrics? • Do these critical metrics differ across genres? • How much does optimizing a metric help?

  5. Datasets and Metrics • Data Collection • A week of data from multiple premium video sites & full census measurement from video player • Video Genres • Live • LVoD • SVoD

  6. Datasets and Metrics • Quality Metrics • Buffering Ratio • Rate of Buffering • Join time • Rendering Quality • Average Bit Rate

  7. Datasets and Metrics • Two Engagement Granularities • View • Play time of a video session • Viewer • Total play time by a viewer in a period of time • Total number of views by a viewer in a period of time

  8. Analysis Techniques • Which metrics matter most • Are metrics independent? • How do we quantify the impact?

  9. Analysis Techniques • Qualitative • Correlation Coefficient • Information Gain • Linear Regression • Quantitative

  10. Analysis Techniques • An simple example

  11. View Level Engagement • Long VoD Content - Correlation

  12. View Level Engagement • Long VoDContent - Correlation • Most important metric • Buffering ratio • Less important metrics • Rendering quality, Join time

  13. View Level Engagement • Long VoD Content – Information Gain

  14. View Level Engagement • Long VoD Content – Information Gain • Bit rate becomes the most important metric • Why??????

  15. View Level Engagement • Live Content

  16. View Level Engagement • Live Content • Buffering Ration remains the most significant • Bitrate and Rate of Buffering matter much more

  17. View Level Engagement • Live Content • Rendering Quality negatively correlated?! • User behavior matters

  18. View Level Engagement • Short VoD Content

  19. View Level Engagement • Short VoDContent • Similar to long VoD content • Buffering ration remains the strongest • Rendering Quality is less important

  20. View Level Engagement • Quantitative Impact • Not apply regression to all the data • Only apply regression to the segment that looks like linear • 0-10% range of Buffering ratio

  21. View Level Engagement • Summary • BufRatiois the most important quality metric. • For live content, AvgBitrate in addition to BufRatio is a key quality metric. • A 1% increase in BufRatio can decrease 1 to 3 minutes ofviewing time. • JoinTime has significantly lower impact on view-level engagement than the other metrics • RendQualin live video highlights the need of considering context of actual user and system behavior

  22. Viewer Level Engagement • Buffering ratio vs. play time

  23. Viewer Level Engagement • Buffering ratio vs. # of views

  24. Viewer Level Engagement • Summary • Both the # of views and the total play time are impacted by the quality metrics • Correlation between the engagement metrics and the quality metrics becomes visually and quantitatively more striking at the viewer level • The join time, which seemed less relevant at the view level, has non-trivial impact at the viewer level

  25. Lesson Learned • The need for complementary analysis • All of you are right. The reason every one of you is telling it differently is because each one of you touched a different part of the elephant. So, actually the elephant has all the features you mentioned. • Combination of Correlation and Information gain

  26. Lesson Learned • The importance of context • Lies, damned lies, and statistics • Together with the context of the human and operating factors

  27. Lesson Learned • Toward video quality index • Provide objective index for service providers and researchersex: MOS • More dimensions • More play type • More Content type • Etc…

  28. Conclusion

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