short-paper

A scalable video coding dataset and toolchain for dynamic adaptive streaming over HTTP

Authors:

Christian Kreuzberger,

Hermann HellwagnerAuthors Info & Claims

MMSys '15: Proceedings of the 6th ACM Multimedia Systems Conference

Pages 213 - 218

https://doi.org/10.1145/2713168.2713193

Published: 18 March 2015 Publication History

Abstract

With video streaming becoming more and more popular, the number of devices that are capable of streaming videos over the Internet is growing. This leads to a heterogeneous device landscape with varying demands. Dynamic Adaptive Streaming over HTTP (DASH) offers an elegant solution to these demands. Smart adaptation logics are able to adjust the clients' streaming quality according to several (local) parameters. Recent research indicated benefits of blending Scalable Video Coding (SVC) with DASH, especially considering Future Internet architectures. However, except for a DASH dataset with a single SVC encoded video, no other datasets are publicly available. The contribution of this paper is two-fold. First, a DASH/SVC dataset, containing multiple videos at varying bitrates and spatial resolutions including 1080p, is presented. Second, a toolchain for multiplexing SVC encoded videos is provided, therefore making our results reproducible and allowing researchers to generate their own datasets.

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Cited By

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https://doi.org/10.3390/sym17020194
Guo BChen YCheng PDing MHu JHanzo L(2024)Pareto-Optimal Multiagent Cooperative Caching Relying on Multipolicy Reinforcement LearningIEEE Internet of Things Journal10.1109/JIOT.2023.331797111:5(7904-7917)Online publication date: 1-Mar-2024
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Published In

cover image ACM Conferences

MMSys '15: Proceedings of the 6th ACM Multimedia Systems Conference

March 2015

277 pages

ISBN:9781450333511

DOI:10.1145/2713168

General Chair:
Wei Tsang Ooi
National University of Singapore, Singapore
,
Program Chairs:
Wu-chi Feng
Portland State University
,
Feng Liu
Portland State University

Copyright © 2015 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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Publication History

Published: 18 March 2015

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Short-paper

Funding Sources

Austrian Science Fund
CHIST-ERA

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MMSys '15

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MMSys '15: Multimedia Systems Conference 2015

March 18 - 20, 2015

Oregon, Portland

Acceptance Rates

MMSys '15 Paper Acceptance Rate 12 of 41 submissions, 29%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Wang WWei XTao WZhou MJi C(2025)Quality of Experience-Oriented Cloud-Edge Dynamic Adaptive Streaming: Recent Advances, Challenges, and OpportunitiesSymmetry10.3390/sym1702019417:2(194)Online publication date: 26-Jan-2025
https://doi.org/10.3390/sym17020194
Guo BChen YCheng PDing MHu JHanzo L(2024)Pareto-Optimal Multiagent Cooperative Caching Relying on Multipolicy Reinforcement LearningIEEE Internet of Things Journal10.1109/JIOT.2023.331797111:5(7904-7917)Online publication date: 1-Mar-2024
https://doi.org/10.1109/JIOT.2023.3317971
Zhong LWang MXu CYang SMuntean G(2023)Decentralized Optimization for Multicast Adaptive Video Streaming in Edge Cache-Assisted NetworksIEEE Transactions on Broadcasting10.1109/TBC.2023.325416569:3(812-822)Online publication date: Sep-2023
https://doi.org/10.1109/TBC.2023.3254165
Ozcan SSayit M(2023)Multipath transmission aware ABR algorithm for SVC HASComputer Communications10.1016/j.comcom.2023.01.015201(20-36)Online publication date: Mar-2023
https://doi.org/10.1016/j.comcom.2023.01.015
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
Houidi OBakri SZeghlache D(2022)Multi-Agent Graph Convolutional Reinforcement Learning for Intelligent Load BalancingNOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS54207.2022.9789872(1-6)Online publication date: 25-Apr-2022
https://doi.org/10.1109/NOMS54207.2022.9789872
Ma JShang BSong HHuang YFan P(2022)Reliability Versus Latency in IIoT Visual Applications: A Scalable Task Offloading FrameworkIEEE Internet of Things Journal10.1109/JIOT.2022.31481159:17(16726-16735)Online publication date: 1-Sep-2022
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Houidi OZeghlache DPerrier VAnh Quang PHuin NLeguay JMedagliani P(2022)Constrained Deep Reinforcement Learning for Smart Load Balancing2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC49033.2022.9700657(207-215)Online publication date: 8-Jan-2022
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Hewage CAhmad AMallikarachchi TBarman NMartini M(2022)Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open ChallengesIEEE Access10.1109/ACCESS.2022.318049110(60267-60293)Online publication date: 2022
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Kumar SWang NRahulan YEvans B(2022)Edge Computing-Based Layered Video Streaming Over Integrated Satellite and Terrestrial 5G NetworksIEEE Access10.1109/ACCESS.2022.315199810(19971-19985)Online publication date: 2022
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