QoE Ready to Respond: A QoE-aware MEC Selection Scheme for DASH-based Adaptive Video Streaming to Mobile Users

Wanxin Shi; Qing Li; Ruishan Zhang; Gengbiao Shen; Yong Jiang; Zhenhui Yuan; Gabriel-Miro Muntean

doi:10.1145/3474085.3475325

QoE Ready to Respond: A QoE-aware MEC Selection Scheme for DASH-based Adaptive Video Streaming to Mobile Users

Wanxin Shi, Qing Li^*, Ruishan Zhang, Gengbiao Shen, Yong Jiang, Zhenhui Yuan, Gabriel-Miro Muntean

^*Corresponding author for this work

Computer Information Sciences Department

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

121 Downloads (Pure)

Abstract

The Multi-access Edge Computing (MEC) paradigm offers cloud-computing support to rich media applications, including Dynamic Adaptive Streaming over HTTP (DASH)-based ones at the edge of the network, close to mobile users. MEC servers, typically deployed at base stations (BS), help reduce latency and improve quality of experience (QoE) of video streaming. Unfortunately the communications involving mobile users require handovers between BSs and these influence both transmission efficiency because of the relative position of the MEC servers and transit cost. At the same time, serving MEC for a mobile user should not necessarily be changed when handover occurs. This paper introduces QoE Ready to Respond (QoE-R2R), a QoE-aware MEC Selection scheme for DASH-based mobile adaptive video streaming for optimizing video transmission in a MEC-supported network environment. Simulation-based testing shows that the proposed (QoE-R2R) scheme outperforms some traditional alternative solutions. Compared to hit rate and delay-based schemes, QoE-R2R reduces by 27.6% transmission time and improves with 6.2% QoE.

Original language	English
Title of host publication	MM 2021
Subtitle of host publication	Proceedings of the 29th ACM International Conference on Multimedia
Place of Publication	New York
Publisher	ACM
Pages	4016–4024
Number of pages	9
ISBN (Electronic)	9781450386517
DOIs	https://doi.org/10.1145/3474085.3475325
Publication status	Published - 17 Oct 2021
Event	MM '21: ACM Multimedia Conference 2021 - Hybrid (Onsite and Virtual), Chengdu, China Duration: 20 Oct 2021 → 24 Oct 2021 https://2021.acmmm.org/

Conference

Conference	MM '21
Country/Territory	China
City	Chengdu
Period	20/10/21 → 24/10/21
Internet address	https://2021.acmmm.org/

Keywords

DASH
MEC selection
handover
mobile users

Access to Document

10.1145/3474085.3475325

sample-sigconfAccepted author manuscript, 1.52 MB

Cite this

@inproceedings{3f23f0843ca54e119a0ec26190f56a2d,

title = "QoE Ready to Respond: A QoE-aware MEC Selection Scheme for DASH-based Adaptive Video Streaming to Mobile Users",

abstract = "The Multi-access Edge Computing (MEC) paradigm offers cloud-computing support to rich media applications, including Dynamic Adaptive Streaming over HTTP (DASH)-based ones at the edge of the network, close to mobile users. MEC servers, typically deployed at base stations (BS), help reduce latency and improve quality of experience (QoE) of video streaming. Unfortunately the communications involving mobile users require handovers between BSs and these influence both transmission efficiency because of the relative position of the MEC servers and transit cost. At the same time, serving MEC for a mobile user should not necessarily be changed when handover occurs. This paper introduces QoE Ready to Respond (QoE-R2R), a QoE-aware MEC Selection scheme for DASH-based mobile adaptive video streaming for optimizing video transmission in a MEC-supported network environment. Simulation-based testing shows that the proposed (QoE-R2R) scheme outperforms some traditional alternative solutions. Compared to hit rate and delay-based schemes, QoE-R2R reduces by 27.6% transmission time and improves with 6.2% QoE.",

keywords = "DASH, MEC selection, handover, mobile users",

author = "Wanxin Shi and Qing Li and Ruishan Zhang and Gengbiao Shen and Yong Jiang and Zhenhui Yuan and Gabriel-Miro Muntean",

note = "Funding information: This work is supported by the Key-Area Research and Development Program of Guangdong Province under grant No. 2020B0101130006, National Natural Science Foundation of China under grant No. 61972189, Guangdong Province Key Area R&D Program under grant No. 2018B010113001, the Shenzhen Key Lab of Software De$ned Networking under grant No. ZDSYS20140509172959989 and Science Foundation Ireland (SFI) via grant number 12/RC/2289_P2.; MM '21 : ACM Multimedia Conference 2021 ; Conference date: 20-10-2021 Through 24-10-2021",

year = "2021",

month = oct,

day = "17",

doi = "10.1145/3474085.3475325",

language = "English",

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Shi, W, Li, Q, Zhang, R, Shen, G, Jiang, Y, Yuan, Z & Muntean, G-M 2021, QoE Ready to Respond: A QoE-aware MEC Selection Scheme for DASH-based Adaptive Video Streaming to Mobile Users. in MM 2021: Proceedings of the 29th ACM International Conference on Multimedia. ACM, New York, pp. 4016–4024, MM '21, Chengdu, China, 20/10/21. https://doi.org/10.1145/3474085.3475325

QoE Ready to Respond: A QoE-aware MEC Selection Scheme for DASH-based Adaptive Video Streaming to Mobile Users. / Shi, Wanxin; Li, Qing; Zhang, Ruishan et al.
MM 2021: Proceedings of the 29th ACM International Conference on Multimedia. New York: ACM, 2021. p. 4016–4024.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - QoE Ready to Respond

T2 - MM '21

AU - Shi, Wanxin

AU - Li, Qing

AU - Zhang, Ruishan

AU - Shen, Gengbiao

AU - Jiang, Yong

AU - Yuan, Zhenhui

AU - Muntean, Gabriel-Miro

N1 - Funding information: This work is supported by the Key-Area Research and Development Program of Guangdong Province under grant No. 2020B0101130006, National Natural Science Foundation of China under grant No. 61972189, Guangdong Province Key Area R&D Program under grant No. 2018B010113001, the Shenzhen Key Lab of Software De$ned Networking under grant No. ZDSYS20140509172959989 and Science Foundation Ireland (SFI) via grant number 12/RC/2289_P2.

PY - 2021/10/17

Y1 - 2021/10/17

N2 - The Multi-access Edge Computing (MEC) paradigm offers cloud-computing support to rich media applications, including Dynamic Adaptive Streaming over HTTP (DASH)-based ones at the edge of the network, close to mobile users. MEC servers, typically deployed at base stations (BS), help reduce latency and improve quality of experience (QoE) of video streaming. Unfortunately the communications involving mobile users require handovers between BSs and these influence both transmission efficiency because of the relative position of the MEC servers and transit cost. At the same time, serving MEC for a mobile user should not necessarily be changed when handover occurs. This paper introduces QoE Ready to Respond (QoE-R2R), a QoE-aware MEC Selection scheme for DASH-based mobile adaptive video streaming for optimizing video transmission in a MEC-supported network environment. Simulation-based testing shows that the proposed (QoE-R2R) scheme outperforms some traditional alternative solutions. Compared to hit rate and delay-based schemes, QoE-R2R reduces by 27.6% transmission time and improves with 6.2% QoE.

AB - The Multi-access Edge Computing (MEC) paradigm offers cloud-computing support to rich media applications, including Dynamic Adaptive Streaming over HTTP (DASH)-based ones at the edge of the network, close to mobile users. MEC servers, typically deployed at base stations (BS), help reduce latency and improve quality of experience (QoE) of video streaming. Unfortunately the communications involving mobile users require handovers between BSs and these influence both transmission efficiency because of the relative position of the MEC servers and transit cost. At the same time, serving MEC for a mobile user should not necessarily be changed when handover occurs. This paper introduces QoE Ready to Respond (QoE-R2R), a QoE-aware MEC Selection scheme for DASH-based mobile adaptive video streaming for optimizing video transmission in a MEC-supported network environment. Simulation-based testing shows that the proposed (QoE-R2R) scheme outperforms some traditional alternative solutions. Compared to hit rate and delay-based schemes, QoE-R2R reduces by 27.6% transmission time and improves with 6.2% QoE.

KW - DASH

KW - MEC selection

KW - handover

KW - mobile users

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U2 - 10.1145/3474085.3475325

DO - 10.1145/3474085.3475325

M3 - Conference contribution

SP - 4016

EP - 4024

BT - MM 2021

PB - ACM

CY - New York

Y2 - 20 October 2021 through 24 October 2021

ER -

QoE Ready to Respond: A QoE-aware MEC Selection Scheme for DASH-based Adaptive Video Streaming to Mobile Users

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Keywords

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