More Web Proxy on the site http://driver.im/

research-article

Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-World Environments using Crowdsourcing

Authors:

Benjamin Rainer,

Christian TimmererAuthors Info & Claims

VideoNext '14: Proceedings of the 2014 Workshop on Design, Quality and Deployment of Adaptive Video Streaming

Pages 19 - 24

https://doi.org/10.1145/2676652.2676656

Published: 02 December 2014 Publication History

Abstract

Multimedia streaming over HTTP has gained momentum with the approval of the MPEG-DASH standard and many research papers evaluated various aspects thereof but mainly within controlled environments. However, the actual behaviour of a DASH client within real-world environments has not yet been evaluated. The aim of this paper is to compare the QoE performance of existing DASH-based Web clients within real-world environments using crowdsourcing. Therefore, we select Google's YouTube player and two open source implementations of the MPEG-DASH standard, namely the DASH-JS from Alpen-Adria-Universitaet Klagenfurt and the dash.js which is the official reference client of the DASH Industry Forum. Based on a predefined content configuration, which is comparable among the clients, we run a crowdsourcing campaign to determine the QoE of each implementation in order to determine the current state-of-the-art for MPEG-DASH systems within real-world environments. The gathered data and its analysis will be presented in the paper. It provides insights with respect to the QoE performance of current Web-based adaptive HTTP streaming systems.

References

[1]

Rec. ITU-R BT.500--11. Technical report.

[2]

A. Colwell, A. Bateman, and M. Watson. Media Source Extensions. W3C Candidate Recommendation, July 2014.

[3]

F. De Simone and F. Dufaux. Comparison of DASH Adaptation Strategies based on Bitrate and Quality Signalling. In Multimedia Signal Processing (MMSP), 2013 IEEE 15th International Workshop on, pages 087--092, Sept 2013.

[4]

P. Eckersley. How unique is your web browser? In M. Atallah and N. Hopper, editors, Privacy Enhancing Technologies, volume 6205 of Lecture Notes in Computer Science, pages 1--18. Springer Berlin Heidelberg, 2010.

Digital Library

[5]

M. Hirth, T. Hossfeld, and P. Tran-Gia. Anatomy of a Crowdsourcing Platform - Using the Example of Microworkers.com. In Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2011 Fifth International Conference on, pages 322--329, June 2011.

Digital Library

[6]

T. Hossfeld, C. Keimel, M. Hirth, B. Gardlo, J. Habigt, K. Diepold, and P. Tran-Gia. Best Practices for QoE Crowdtesting: QoE Assessment With Crowdsourcing. Multimedia, IEEE Transactions on, 16(2):541--558, Feb 2014.

Digital Library

[7]

T. Hossfeld, M. Seufert, M. Hirth, T. Zinner, P. Tran-Gia, and R. Schatz. Quantification of YouTube QoE via Crowdsourcing. In IEEE International Symposium on Multimedia (ISM) 2011, pages 494{499, 2011.

Digital Library

[8]

S. S. Krishnan and R. K. Sitaraman. Video Stream Quality Impacts Viewer Behavior: Inferring Causality Using Quasi-experimental Designs. In Proceedings of the 2012 ACM Conference on Internet Measurement Conference, IMC'12, pages 211--224. ACM, 2012.

Digital Library

[9]

D. Krishnappa, D. Bhat, and M. Zink. DASHing YouTube: An Analysis of using DASH in YouTube Video Service. In Local Computer Networks (LCN), 2013 IEEE 38th Conference on, pages 407--415, Oct 2013.

[10]

Microworkers. http://www.microworkers.com.

[11]

C. Mueller, S. Lederer, and C. Timmerer. An Evaluation of Dynamic Adaptive Streaming over HTTP in Vehicular Environments. In Proceedings of the Fourth Annual ACM SIGMM Workshop on Mobile Video (MoVid12), pages 37--42. ACM, feb 2012.

Digital Library

[12]

N. Nikiforakis, A. Kapravelos, W. Joosen, C. Kruegel, F. Piessens, and G. Vigna. Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting. In Security and Privacy (SP), 2013 IEEE Symposium on, pages 541--555, May 2013.

Digital Library

[13]

B. Rainer, S. Lederer, C. Mueller, and C. Timmerer. A Seamless Web Integration of Adaptive HTTP streaming. In B. Pesquet-Popescu and C. Burileanu, editors, Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pages 1519--1523. European Signal Processing (EURASIP) Society, aug 2012.

[14]

B. Rainer, M. Waltl, and C. Timmerer. A Web based Subjective Evaluation Platform. In Proceedings of the 5th International Workshop on Quality of Multimedia Experience (QoMEX'13), pages 24--25. IEEE, jul 2013.

[15]

I. Sodagar. The MPEG-DASH Standard for Multimedia Streaming Over the Internet. IEEE MultiMedia, 18(4):62--67, 2011.

Digital Library

[16]

Tears of Steel. http://tearsofsteel.org/.

[17]

N. Weil. The State of MPEG-DASH Deployment. Streaming Media Europe, April 2014.

[18]

C.-C. Wu, K.-T. Chen, Y.-C. Chang, and C.-L. Lei. Crowdsourcing Multimedia QoE Evaluation: ATrusted Framework. IEEE Transactions on Multimedia, 15(5):1121--1137, Aug 2013.

Digital Library

Cited By

Hodžić KCosovic MMrdovic SQuinlan JRaca D(2024)DashReStreamer: Framework for Creation of Impaired Video Clips under Realistic Network ConditionsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364001621:1(1-26)Online publication date: 16-Dec-2024
https://dl.acm.org/doi/10.1145/3640016
Peroni LGorinsky STashtarian FTimmerer C(2023)Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming QualityProceedings of the ACM on Networking10.1145/36291391:CoNEXT3(1-27)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3629139
Noh HSong H(2023) HA 2 RS: HTTP Adaptive Augmented Reality Streaming System IEEE Transactions on Mobile Computing10.1109/TMC.2021.313266522:5(2741-2755)Online publication date: 1-May-2023
https://doi.org/10.1109/TMC.2021.3132665
Show More Cited By

Recommendations

QoE for Mobile Clients with Segment-aware Rate Adaptation Algorithm (SARA) for DASH Video Streaming

Dynamic adaptive streaming over HTTP (DASH) is widely used for video streaming on mobile devices. Ensuring a good quality of experience (QoE) for mobile video streaming is essential, as it severely impacts both the network and content providers’ ...
An optimal scheduling algorithm for DASH video streaming over variable bit rate networks

With the rapid increase of network bandwidth, it becomes popular for people to watch video over the internet. To cope with dynamic and heterogeneous network condition, commercial service providers use DASH streaming technology to serve contents to their ...
On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments

Video streaming services are offered over the Internet and since the service providers do not have full control over the network conditions all the way to the end user, streaming technologies have been developed to maintain the quality of service in ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

VideoNext '14: Proceedings of the 2014 Workshop on Design, Quality and Deployment of Adaptive Video Streaming

December 2014

56 pages

ISBN:9781450332811

DOI:10.1145/2676652

Program Chairs:
Mahbub Hassan
University of New South Wales, Australia
,
Ali C. Begen
Cisco Canada
,
Christian Timmerer
Alpen-Adria-Universität Klagenfurt, Austria

Copyright © 2014 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]

Sponsors

SIGCOMM: ACM Special Interest Group on Data Communication

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 December 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

CoNEXT '14

Sponsor:

SIGCOMM

CoNEXT '14: Conference on emerging Networking Experiments and Technologies

December 2, 2014

Sydney, Australia

Acceptance Rates

VideoNext '14 Paper Acceptance Rate 6 of 9 submissions, 67%;

Overall Acceptance Rate 6 of 9 submissions, 67%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
248
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)1

Reflects downloads up to 31 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hodžić KCosovic MMrdovic SQuinlan JRaca D(2024)DashReStreamer: Framework for Creation of Impaired Video Clips under Realistic Network ConditionsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364001621:1(1-26)Online publication date: 16-Dec-2024
https://dl.acm.org/doi/10.1145/3640016
Peroni LGorinsky STashtarian FTimmerer C(2023)Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming QualityProceedings of the ACM on Networking10.1145/36291391:CoNEXT3(1-27)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3629139
Noh HSong H(2023) HA 2 RS: HTTP Adaptive Augmented Reality Streaming System IEEE Transactions on Mobile Computing10.1109/TMC.2021.313266522:5(2741-2755)Online publication date: 1-May-2023
https://doi.org/10.1109/TMC.2021.3132665
Göring SRao RRaake A(2023)Quality assessment of higher resolution images and videos with remote testingQuality and User Experience10.1007/s41233-023-00055-68:1Online publication date: 13-Apr-2023
https://doi.org/10.1007/s41233-023-00055-6
Bampis CLi ZKatsavounidis IHuang TEkanadham CBovik A(2021)Towards Perceptually Optimized Adaptive Video Streaming-A Realistic Quality of Experience DatabaseIEEE Transactions on Image Processing10.1109/TIP.2021.307329430(5182-5197)Online publication date: 2021
https://doi.org/10.1109/TIP.2021.3073294
Rao RGoring SRaake A(2021)Towards High Resolution Video Quality Assessment in the Crowd2021 13th International Conference on Quality of Multimedia Experience (QoMEX)10.1109/QoMEX51781.2021.9465425(1-6)Online publication date: 14-Jun-2021
https://doi.org/10.1109/QoMEX51781.2021.9465425
Goring SRamachandra Rao RFremerey SRaake A(2021)AVrate Voyager: an open source online testing platform2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP53017.2021.9733561(1-6)Online publication date: 6-Oct-2021
https://doi.org/10.1109/MMSP53017.2021.9733561
Seufert ASchröder SSeufert M(2021)Delivering User Experience over Networks: Towards a Quality of Experience Centered Design Cycle for Improved Design of Networked ApplicationsSN Computer Science10.1007/s42979-021-00851-x2:6Online publication date: 16-Sep-2021
https://doi.org/10.1007/s42979-021-00851-x
Bouraqia KSabir ESadik M(2017)YouTube context-awareness to enhance Quality of experience between yesterday, today and tomorrow: Survey2017 International Conference on Wireless Networks and Mobile Communications (WINCOM)10.1109/WINCOM.2017.8238187(1-7)Online publication date: Nov-2017
https://doi.org/10.1109/WINCOM.2017.8238187
ZHU W(2017)Message From the Incoming Editor-in-ChiefIEEE Transactions on Multimedia10.1109/TMM.2017.265741819:3(446-446)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1109/TMM.2017.2657418
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten