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Exploring Temporal Loss Tolerance of Video Codecs for QoE Enhancement in Adaptive Streaming

Abstract

dc:description.abstract

Video streaming is not only for entertainment now, but also for lessons, communications, meetings, and even diagnosis. Quality of Experience (QoE) is the most important concerned aspect by content providers and platform providers. Among all the experience, enduring stalls is the most frustrating one. In this thesis, we present Bandwidth-Efficient Temporal Adaptation (BETA) and Temporal Adaptive Streaming over QUIC (TASQ), whose target is to improve the smoothness of video playbacks even under extreme network conditions. We investigate temporal loss tolerance for HEVC and AV1, and apply the optimization on video streaming. Results show a drastic improvement on the smoothness and the QoE.

Degree

thesis:*
Name thesis:degree_name
Master of Science (MSc)
Discipline thesis:degree_discipline
Computer Science
Grantor dc:publisher.institution
Science
Year dc:date.issued
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Liu, Yang
Advisor dc:contributor.advisor
  • Wang, Mea
Committee members dc:contributor.committeemember
  • Alim, Usman
  • Krishnamurthy, Diwakar

Subjects

dc:subject × 3

Rights

dc:rights
Statement dc:rights
  • University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
OAI identifier oai:identifier
oai:ucalgary.scholaris.ca:1880/113787

Chain of custody

source
Harvested from
University of Calgary
Base URL
ucalgary.scholaris.ca/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Liu, Yang. Exploring Temporal Loss Tolerance of Video Codecs for QoE Enhancement in Adaptive Streaming. Science, 2021. http://hdl.handle.net/1880/113787