Towards Perceptually Optimized End-to-end Adaptive Video Streaming

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.

References in corpus (1)

• Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale

Cited by in corpus (5)

• Adaptive Video Encoding For Different Video Codecs
• Assessing the Quality-of-Experience of Adaptive Bitrate Video Streaming
• NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder
• SENSEI: Aligning Video Streaming Quality with Dynamic User Sensitivity
• A Knowledge-Driven Quality-of-Experience Model for Adaptive Streaming Videos