Towards Perceptually Optimized End-to-end Adaptive Video Streaming

TL;DR

This paper introduces the LIVE-NFLX-II database, a comprehensive resource for studying subjective QoE in adaptive video streaming, enabling the development of perceptually optimized streaming algorithms.

Contribution

The paper presents a new realistic QoE database with diverse content, network conditions, and adaptation algorithms, facilitating research on perceptually optimized streaming strategies.

Findings

Analyzed effects of streaming dimensions on user experience

Evaluated video quality and QoE models using the database

Provided a publicly available resource for future research

Abstract

Measuring Quality of Experience (QoE) and integrating these measurements into video streaming algorithms is a multi-faceted problem that fundamentally requires the design of comprehensive subjective QoE databases and metrics. To achieve this goal, we have recently designed the LIVE-NFLX-II database, a highly-realistic database which contains subjective QoE responses to various design dimensions, such as bitrate adaptation algorithms, network conditions and video content. Our database builds on recent advancements in content-adaptive encoding and incorporates actual network traces to capture realistic network variations on the client device. Using our database, we study the effects of multiple streaming dimensions on user experience and evaluate video quality and quality of experience models. We believe that the tools introduced here will help inspire further progress on the development of perceptually-optimized client adaptation and video streaming strategies. The database is publicly available at http://live.ece.utexas.edu/research/LIVE_NFLX_II/live_nflx_plus.html.