research-article

Distributed DASH dataset

Authors:

Stefan Lederer,

Christopher Mueller,

Christian Timmerer,

Cyril Concolato,

Jean Le Feuvre,

Karel FliegelAuthors Info & Claims

MMSys '13: Proceedings of the 4th ACM Multimedia Systems Conference

Pages 131 - 135

https://doi.org/10.1145/2483977.2483994

Published: 28 February 2013 Publication History

Abstract

The delivery of multimedia content over HTTP and on top of existing Internet infrastructures is becoming the preferred method within heterogeneous environment. The basic design principle is having an intelligent client which selects given and applicable media representations by issuing HTTP requests for individual segments based on the users' context and current conditions. Typically, this client behavior differs between implementations of the same kind and for the objective evaluations thereof appropriate datasets are needed. This paper presents a distributed dataset for the recently published MPEG-DASH standard which is mirrored at different sites across Europe, namely Klagenfurt, Paris, and Prague. A client implementation may choose to request segments from these sites and dynamically switch to a different location, e.g., in case the one currently used causes any issues. Thus, this distributed DASH dataset can be used for real-world evaluations enabling the simulation of switching between different content delivery networks.

References

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Digital Library

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Cited By

Afifi HPochaba SBoltres ALaniewski DHaberer JPaeleke LPoorzare RStolpmann DWehner NRedder ASamikwa ESeufert M(2024)Machine Learning With Computer Networks: Techniques, Datasets, and ModelsIEEE Access10.1109/ACCESS.2024.338446012(54673-54720)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3384460
Jiang WWang KNing PRen ZHu JWang J(2024)Improve video QoE by practical bandwidth allocationMultimedia Systems10.1007/s00530-024-01577-530:6Online publication date: 27-Nov-2024
https://doi.org/10.1007/s00530-024-01577-5
Taraghi BAmirpour HTimmerer CMurray NSimon GFarias MViola IMontagud M(2022)Multi-codec ultra high definition 8K MPEG-DASH datasetProceedings of the 13th ACM Multimedia Systems Conference10.1145/3524273.3532889(216-220)Online publication date: 14-Jun-2022
https://dl.acm.org/doi/10.1145/3524273.3532889
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Index Terms

Distributed DASH dataset
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases

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Published In

cover image ACM Conferences

MMSys '13: Proceedings of the 4th ACM Multimedia Systems Conference

February 2013

304 pages

ISBN:9781450318945

DOI:10.1145/2483977

General Chair:
Carsten Griwodz
Simula Research Laboratory & University of Oslo, Norway

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 February 2013

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Research-article

Funding Sources

Czech Science Foundation
French funded project AUSTRAL (DGCIS FUI13)
European Cooperation in Science and Technology
Seventh Framework Programme
Czech-funded project COST CZ LD12018 MOVERIQ

Conference

MMSys '13

Sponsor:

MMSys '13: Multimedia Systems Conference 2013

February 28 - March 1, 2013

Oslo, Norway

Acceptance Rates

MMSys '13 Paper Acceptance Rate 15 of 63 submissions, 24%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Afifi HPochaba SBoltres ALaniewski DHaberer JPaeleke LPoorzare RStolpmann DWehner NRedder ASamikwa ESeufert M(2024)Machine Learning With Computer Networks: Techniques, Datasets, and ModelsIEEE Access10.1109/ACCESS.2024.338446012(54673-54720)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3384460
Jiang WWang KNing PRen ZHu JWang J(2024)Improve video QoE by practical bandwidth allocationMultimedia Systems10.1007/s00530-024-01577-530:6Online publication date: 27-Nov-2024
https://doi.org/10.1007/s00530-024-01577-5
Taraghi BAmirpour HTimmerer CMurray NSimon GFarias MViola IMontagud M(2022)Multi-codec ultra high definition 8K MPEG-DASH datasetProceedings of the 13th ACM Multimedia Systems Conference10.1145/3524273.3532889(216-220)Online publication date: 14-Jun-2022
https://dl.acm.org/doi/10.1145/3524273.3532889
Fadhil DOliveira R(2022)A Novel Packet End-to-End Delay Estimation Method for Heterogeneous NetworksIEEE Access10.1109/ACCESS.2022.318811610(71387-71397)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3188116
Viola RZorrilla MAngueira PMontalbán J(2022)Multi-access Edge Computing video analytics of ITU-T P.1203 Quality of Experience for streaming monitoring in dense client cellsMultimedia Tools and Applications10.1007/s11042-022-12537-481:9(12387-12403)Online publication date: 3-Mar-2022
https://doi.org/10.1007/s11042-022-12537-4
Viola RMartin AMorgade JMasneri SZorrilla MAngueira PMontalban J(2021)Predictive CDN Selection for Video Delivery Based on LSTM Network Performance Forecasts and Cost-Effective Trade-OffsIEEE Transactions on Broadcasting10.1109/TBC.2020.303172467:1(145-158)Online publication date: Mar-2021
https://doi.org/10.1109/TBC.2020.3031724
Vlaović JRimac-Drlje SŽagar DFilipović L(2021)Content dependent spatial resolution selection for MPEG DASH segmentationJournal of Industrial Information Integration10.1016/j.jii.2021.100240(100240)Online publication date: Jul-2021
https://doi.org/10.1016/j.jii.2021.100240
Zhang SLiu GXu JWan S(2021)Design and analysis of a fair, efficient and stable multi-server adaptive streaming protocolComputers and Electrical Engineering10.1016/j.compeleceng.2021.10737195:COnline publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.compeleceng.2021.107371
Agrawal PZabrovskiy AIlangovan ATimmerer CProdan R(2021)FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videosCluster Computing10.1007/s10586-020-03207-x24:3(1605-1621)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s10586-020-03207-x
Jiang WNing PZhang ZHu JRen ZWang J(2021)Practical Bandwidth Allocation for Video QoE FairnessWireless Algorithms, Systems, and Applications10.1007/978-3-030-85928-2_41(523-534)Online publication date: 2-Sep-2021
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