Abstract

HTTP Adaptive Streaming (HAS) has been becoming a de facto standard for Over-the-top (OTT) video services. Typically, by adapting to network conditions, it provides smoother video quality perceived by the end users. However, when the network condition always fluctuates due to some reasons (e.g. bandwidth competition among HAS player or between HAS player and other applications), the perceived video quality might be deteriorated. This demands an effective approach to maintain specific Quality of Experience (QoE) level for the users. To do so, available bandwidth allocation is chosen as a common QoE control method. However, accurately allocating available bandwidth is still a challenge. In this paper, bandwidth allocation based on the relation between subjective Mean Opinions Score (MOS) and requested bitrate is proposed. The relation is captured by a regression model, which is applied to estimate the needed available bandwidth for the users. As the result of controlling the bandwidth, the users start to request the encoding bitrate equal to target bitrate after several requests, resulting in higher perceived video quality.

Original languageEnglish
Pages (from-to)83-94
Number of pages12
JournalInternational Journal of Computer Networks and Communications
Volume9
Issue number5
DOIs
Publication statusPublished - 2017 Sep 1

Fingerprint

Frequency allocation
HTTP
Bandwidth

Keywords

  • HTTP adaptive streaming
  • Mean opinion score (MOS)
  • Quality of experience (QoE)
  • Quality of service (QoS)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture

Cite this

@article{e2f878b195e645d3b6261b1475beac24,
title = "Accurate available bandwidth allocation in http adaptive streaming",
abstract = "HTTP Adaptive Streaming (HAS) has been becoming a de facto standard for Over-the-top (OTT) video services. Typically, by adapting to network conditions, it provides smoother video quality perceived by the end users. However, when the network condition always fluctuates due to some reasons (e.g. bandwidth competition among HAS player or between HAS player and other applications), the perceived video quality might be deteriorated. This demands an effective approach to maintain specific Quality of Experience (QoE) level for the users. To do so, available bandwidth allocation is chosen as a common QoE control method. However, accurately allocating available bandwidth is still a challenge. In this paper, bandwidth allocation based on the relation between subjective Mean Opinions Score (MOS) and requested bitrate is proposed. The relation is captured by a regression model, which is applied to estimate the needed available bandwidth for the users. As the result of controlling the bandwidth, the users start to request the encoding bitrate equal to target bitrate after several requests, resulting in higher perceived video quality.",
keywords = "HTTP adaptive streaming, Mean opinion score (MOS), Quality of experience (QoE), Quality of service (QoS)",
author = "{Phan Xuan}, Tan and Eiji Kamioka",
year = "2017",
month = "9",
day = "1",
doi = "10.5121/ijcnc.2017.9507",
language = "English",
volume = "9",
pages = "83--94",
journal = "International Journal of Computer Networks and Communications",
issn = "0975-2293",
publisher = "Academy and Industry Research Collaboration Center (AIRCC)",
number = "5",

}

TY - JOUR

T1 - Accurate available bandwidth allocation in http adaptive streaming

AU - Phan Xuan, Tan

AU - Kamioka, Eiji

PY - 2017/9/1

Y1 - 2017/9/1

N2 - HTTP Adaptive Streaming (HAS) has been becoming a de facto standard for Over-the-top (OTT) video services. Typically, by adapting to network conditions, it provides smoother video quality perceived by the end users. However, when the network condition always fluctuates due to some reasons (e.g. bandwidth competition among HAS player or between HAS player and other applications), the perceived video quality might be deteriorated. This demands an effective approach to maintain specific Quality of Experience (QoE) level for the users. To do so, available bandwidth allocation is chosen as a common QoE control method. However, accurately allocating available bandwidth is still a challenge. In this paper, bandwidth allocation based on the relation between subjective Mean Opinions Score (MOS) and requested bitrate is proposed. The relation is captured by a regression model, which is applied to estimate the needed available bandwidth for the users. As the result of controlling the bandwidth, the users start to request the encoding bitrate equal to target bitrate after several requests, resulting in higher perceived video quality.

AB - HTTP Adaptive Streaming (HAS) has been becoming a de facto standard for Over-the-top (OTT) video services. Typically, by adapting to network conditions, it provides smoother video quality perceived by the end users. However, when the network condition always fluctuates due to some reasons (e.g. bandwidth competition among HAS player or between HAS player and other applications), the perceived video quality might be deteriorated. This demands an effective approach to maintain specific Quality of Experience (QoE) level for the users. To do so, available bandwidth allocation is chosen as a common QoE control method. However, accurately allocating available bandwidth is still a challenge. In this paper, bandwidth allocation based on the relation between subjective Mean Opinions Score (MOS) and requested bitrate is proposed. The relation is captured by a regression model, which is applied to estimate the needed available bandwidth for the users. As the result of controlling the bandwidth, the users start to request the encoding bitrate equal to target bitrate after several requests, resulting in higher perceived video quality.

KW - HTTP adaptive streaming

KW - Mean opinion score (MOS)

KW - Quality of experience (QoE)

KW - Quality of service (QoS)

UR - http://www.scopus.com/inward/record.url?scp=85032972197&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032972197&partnerID=8YFLogxK

U2 - 10.5121/ijcnc.2017.9507

DO - 10.5121/ijcnc.2017.9507

M3 - Article

AN - SCOPUS:85032972197

VL - 9

SP - 83

EP - 94

JO - International Journal of Computer Networks and Communications

JF - International Journal of Computer Networks and Communications

SN - 0975-2293

IS - 5

ER -