Abstract
Today´s packet-switched networks are subject to
bandwidth fluctuations that cause degradation of the user experience
of multimedia services. In order to cope with this problem,
HTTP adaptive streaming (HAS) has been proposed in recent
years as a video delivery solution for the future Internet and
being adopted by an increasing number of streaming services,
such as Netflix and Youtube. HAS enables service providers to
improve users´ quality of experience (QoE) and network resource
utilization by adapting the quality of the video stream to the
current network conditions. However, the resulting time-varying
video quality caused by adaptation introduces a new type of
impairment and thus novel QoE research challenges. Despite
various recent attempts to investigate these challenges, many
fundamental questions regarding HAS perceptual performance
are still open. In this paper, the QoE impact of different
technical adaptation parameters, including chunk length, switching
amplitude, switching frequency, and temporal recency, are
investigated. In addition, the influence of content on perceptual
quality of these parameters is analyzed. To this end, a large
number of adaptation scenarios have been subjectively evaluated
in four laboratory experiments and one crowdsourcing study.
A statistical analysis of the combined data set reveals results
that partly contradict widely held assumptions and provide novel insights in perceptual quality of adapted video sequences,
e.g., interaction effects between quality switching direction
(up/down) and switching strategy (smooth/abrupt). The
large variety of experimental configurations across different
studies ensures the consistency and external validity of the
presented results that can be utilized for enhancing the perceptual
performance of adaptive streaming services.
Originalsprache | Englisch |
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Seiten (von - bis) | 2141-2153 |
Seitenumfang | 13 |
Fachzeitschrift | IEEE Journal on Selected Areas in Communications |
Volume | 34 |
Issue | 8 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2016 |
Research Field
- Ehemaliges Research Field - Technology Experience