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Evaluating perceptual video quality for mobile clients in 802.11n WLAN

Authors:

Victor Omwando,

Prasant MohapatraAuthors Info & Claims

WiNTECH '13: Proceedings of the 8th ACM international workshop on Wireless network testbeds, experimental evaluation & characterization

Pages 57 - 64

https://doi.org/10.1145/2505469.2505476

Published: 30 September 2013 Publication History

Abstract

In this paper, we characterize the performance of HD video streaming in 802.11n WLANs under user mobility. We conducted experiments in QuRiNet, a large-scale outdoor wireless testbed that experiences little electromagnetic interference. We observe the variation in video quality with the variance of both speed of a mobile user and his distance from access point (AP). Using subjective scores and objective video quality assessment metrics, we build a non-linear regression model to estimate video quality based on user speed and distance. An ensemble machine learning kernel, bagging, is used in conjunction with Reduced Error Pruning Decision Trees to build a non-linear prediction model that scores 69% correlation with video quality. Overall, we find that distance has larger impact on video quality than speed. However, the physical factors such as speed and distance cannot be used in isolation to estimate video quality accurately.

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Cited By

Hanford NAhuja VBalman MFarrens MGhosal DPouyoul ETierney BBalman MByna STierney B(2013)Characterizing the impact of end-system affinities on the end-to-end performance of high-speed flowsProceedings of the Third International Workshop on Network-Aware Data Management10.1145/2534695.2534697(1-10)Online publication date: 17-Nov-2013
https://dl.acm.org/doi/10.1145/2534695.2534697
Agrawal AAl-Bahrani RRusso MRaman JChoudhary A(2013)Lung transplant outcome prediction using UNOS data2013 IEEE International Conference on Big Data10.1109/BigData.2013.6691751(1-8)Online publication date: Oct-2013
https://doi.org/10.1109/BigData.2013.6691751

Index Terms

Evaluating perceptual video quality for mobile clients in 802.11n WLAN
1. General and reference
  1. Cross-computing tools and techniques
    1. Performance

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Published In

cover image ACM Conferences

WiNTECH '13: Proceedings of the 8th ACM international workshop on Wireless network testbeds, experimental evaluation & characterization

September 2013

114 pages

ISBN:9781450323642

DOI:10.1145/2505469

General Chairs:
Albert Banchs
University Carlos III and IMDEA Networks, Spain
,
Sunghyun Choi
Seoul National University, Korea

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 September 2013

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MobiCom'13

Sponsor:

SIGMOBILE

MobiCom'13: The 19th Annual International Conference on Mobile Computing and Networking

September 30, 2013

Florida, Miami, USA

Acceptance Rates

WiNTECH '13 Paper Acceptance Rate 11 of 26 submissions, 42%;

Overall Acceptance Rate 63 of 100 submissions, 63%

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Cited By

Hanford NAhuja VBalman MFarrens MGhosal DPouyoul ETierney BBalman MByna STierney B(2013)Characterizing the impact of end-system affinities on the end-to-end performance of high-speed flowsProceedings of the Third International Workshop on Network-Aware Data Management10.1145/2534695.2534697(1-10)Online publication date: 17-Nov-2013
https://dl.acm.org/doi/10.1145/2534695.2534697
Agrawal AAl-Bahrani RRusso MRaman JChoudhary A(2013)Lung transplant outcome prediction using UNOS data2013 IEEE International Conference on Big Data10.1109/BigData.2013.6691751(1-8)Online publication date: Oct-2013
https://doi.org/10.1109/BigData.2013.6691751

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