Developing a Predictive Model ofQuality 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 relea

se

:

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

Evaluation

Dicussion