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

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

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

Contributions Highlighting challenges in obtaining a robust video QoE model

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

Challenges in developing QoE Complex relationships Interaction between metrics Confounding factors

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

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

Machine learning model

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

: Information gain

Approach Overview

Summary of confounding factors

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

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

Evaluation

Dicussion