1. Developing a Predictive Model of Quality of Experience for Internet Video Athula Balachandran -CMU

2. QoE(Quality of Experience) Traditionally –Peak Signal-to-Noise Ratio (PSNR) Now –rate of buffering –bitrate –join time –viewing time –number of visits Fraction of content viewed

3. MOTIVATION Why to improve QoE(Quality of Experience) Advertisement and subscription based revenue... Money

4. Contributions Highlighting challenges in obtaining a robust video QoE model

5. Industry-standard quality metrics Average bitrate Join time Buffering ratio Rate of buffering QoE

6. Challenges in developing QoE Complex relationships Interaction between metrics Confounding factors

7. Contributions Highlighting challenges in obtaining a robust video QoE model A roadmap for developing Internet video QoE that leverages machine learning A methodology for addressing confound- ing factors that affect engagement

8. Roadmap Tackling complex relationships and interdependencies Identifying the important confounding factors Refinement to account for confounding factors

9. Machine learning model

10. Confounding Factors Content attributes –type of video and the overall popularity User attributes –user’s location, device and connectivity Temporal attributes –time of day, day of week and time since release

11. : Information gain

12. Approach Overview

13. Summary of confounding factors

14. Refine the decision tree model Candidate approaches –Add as new feature –Split Data

15. Contributions Highlighting challenges in obtaining a robust video QoE model A roadmap for developing Internet video QoE that leverages machine learning A methodology for addressing confound- ing factors that affect engagement A practical demonstration of the utility of our QoE models to improve engagement

16. Evaluation

18. Dicussion