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demonstration

MUSLIN demo: high QoE fair multi-source live streaming

Authors:

Simon Da Silva,

Joachim Bruneau-Queyreix,

Mathias Lacaud,

Laurent RéveillèreAuthors Info & Claims

MMSys '18: Proceedings of the 9th ACM Multimedia Systems Conference

Pages 529 - 532

https://doi.org/10.1145/3204949.3208108

Published: 12 June 2018 Publication History

Abstract

Delivering video content with a high and fairly shared quality of experience is a challenging task in view of the drastic video traffic increase forecasts. Currently, content delivery networks provide numerous servers hosting replicas of the video content, and consuming clients are re-directed to the closest server. Then, the video content is streamed using adaptive streaming solutions. However, some servers become overloaded, and clients may experience a poor or unfairly distributed quality of experience.

In this demonstration, we showcase Muslin, a streaming solution supporting a high, fairly shared end-users quality of experience for live streaming. Muslin leverages on MS-Stream, a content delivery solution in which a client can simultaneously use several servers. Muslin dynamically provisions servers and replicates content into servers, and advertises servers to clients based on real-time delivery conditions. Our demonstration shows that our approach outperforms traditional content delivery schemes enabling to increase the fairness and quality of experience at the user side without requiring a greater underlying content delivery platform.

References

[1]

2017. MS-Stream Demonstration: http://msstream.net. (2017).

[2]

Cisco. 2016. VNI. (2016). cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.pdf

[3]

A. Flavel et al. 2015. Fastroute: A scalable load-aware anycast routing architecture for modern cdns. connections 27 (2015).

[4]

E. Nygren et al. 2010. The Akamai Network: A Platform for High-performance Internet Applications. SIGOPS Oper. Syst. Rev. (2010).

Digital Library

[5]

J. Bruneau-Queyreix et al. 2017. A multiple-source adaptive streaming solution enhancing consumer's perceived quality. In IEEE Consumer Communications and Networking Conference (CCNC), demonstration track. Las vegas, United States.

[6]

J. Bruneau-Queyreix et al. 2017. MS-Stream: A multiple-source adaptive streaming solution enhancing consumer's perceived quality. In IEEE Consumer Communications and Networking Conference (CCNC). Las vegas, United States.

[7]

J. Bruneau-Queyreix et al. 2017. QoE Enhancement Through Cost-Effective Adaptation Decision Process for Multiple-Server Streaming over HTTP. In IEEE International Conference on Multimedia and Expo (ICME).

[8]

J. Bruneau-Queyreix et al. 2018. Adding a new dimension to HTTP Adaptive Streaming through multiple-source capabilities. In IEEE Multimedia Magazine.

[9]

M. Seufert et al. 2015. A Survey on Quality of Experience of HTTP Adaptive Streaming. IEEE Communications Surveys and Tutorials (2015).

[10]

P. Georgopoulos et al. 2013. Towards Network-wide QoE Fairness using OpenFlow-assisted Adaptive Video Streaming. ACM SIGCOMM Workshop on Future Human-Centric Multimedia Networking (2013).

Digital Library

[11]

S. Petrangeli et al. 2015. QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming. ACM Trans. Multimedia Comput. Commun. Appl. (2015).

Digital Library

[12]

S. Zhang et al. 2015. Presto: Towards fair and efficient HTTP adaptive streaming from multiple servers. IEEE International Conference on Communications (ICC) (2015).

[13]

T. Hobfeld et al. 2011. Quantification of YouTube QoE via Crowd-sourcing. In IEEE International Symposium on Multimedia.

Digital Library

[14]

T. Hoßfeld et al. 2017. Definition of QoE Fairness in Shared Systems. IEEE Communications Letters (2017).

[15]

V. K. Adhikari et al. 2012. Unreeling netflix: Understanding and improving multi-CDN delivery. IEEE INFOCOM (2012).

[16]

W. Pu et al. 2011. Dynamic Adaptive Streaming over HTTP from Multiple Content Distribution Servers. IEEE Global Telecommunications Conference (GLOBECOM) (2011).

[17]

A. Passarella. 2012. A survey on content-centric technologies for the current Internet: CDN and P2P solutions. Computer Communications (2012).

Digital Library

Cited By

Yadav PBentaleb ALim MHuang JOoi WZimmermann RAlay ÖHsu CBegen A(2021)Playing chunk-transferred DASH segments at low latency with QLiveProceedings of the 12th ACM Multimedia Systems Conference10.1145/3458305.3463376(51-64)Online publication date: 24-Jun-2021
https://dl.acm.org/doi/10.1145/3458305.3463376
Yang XHu H(2021)Learning-Based QoE Prediction and Optimization for Video Streaming2021 6th International Conference on Image, Vision and Computing (ICIVC)10.1109/ICIVC52351.2021.9526922(342-346)Online publication date: 23-Jul-2021
https://doi.org/10.1109/ICIVC52351.2021.9526922
Da Silva SBruneau‐Queyreix JLacaud MNegru DRéveillère L(2019)Muslin: A QoE‐aware CDN resources provisioning and advertising system for cost‐efficient multisource live streamingInternational Journal of Network Management10.1002/nem.2081Online publication date: 28-Nov-2019
https://doi.org/10.1002/nem.2081

Index Terms

MUSLIN demo: high QoE fair multi-source live streaming
1. Networks

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Summary
Delivering video content with a high and fairly shared quality of experience is a challenging task in view of the drastic video traffic increase forecasts, as live video traffic will grow 15‐fold by 2022. Currently, content delivery networks ...

High quality streaming is a challenging task in view of the drastic live video traffic increase forecasts (15‐fold growth by 2022). Currently, servers already often become overloaded during major events, and users experience a poor or unfairly distributed ...

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Information & Contributors

Information

Published In

cover image ACM Conferences

MMSys '18: Proceedings of the 9th ACM Multimedia Systems Conference

June 2018

604 pages

ISBN:9781450351928

DOI:10.1145/3204949

General Chair:
Pablo Cesar
Centrum Wiskunde & Informatica, The Netherlands
,
Program Chairs:
Michael Zink
University of Massachusetts Amherst
,
Niall Murray
Athlone Institute of Technology, Ireland

Copyright © 2018 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGMM: ACM Special Interest Group on Multimedia

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2018

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Demonstration

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MMSys '18

Sponsor:

SIGMM

MMSys '18: 9th ACM Multimedia Systems Conference

June 12 - 15, 2018

Amsterdam, Netherlands

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Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Yadav PBentaleb ALim MHuang JOoi WZimmermann RAlay ÖHsu CBegen A(2021)Playing chunk-transferred DASH segments at low latency with QLiveProceedings of the 12th ACM Multimedia Systems Conference10.1145/3458305.3463376(51-64)Online publication date: 24-Jun-2021
https://dl.acm.org/doi/10.1145/3458305.3463376
Yang XHu H(2021)Learning-Based QoE Prediction and Optimization for Video Streaming2021 6th International Conference on Image, Vision and Computing (ICIVC)10.1109/ICIVC52351.2021.9526922(342-346)Online publication date: 23-Jul-2021
https://doi.org/10.1109/ICIVC52351.2021.9526922
Da Silva SBruneau‐Queyreix JLacaud MNegru DRéveillère L(2019)Muslin: A QoE‐aware CDN resources provisioning and advertising system for cost‐efficient multisource live streamingInternational Journal of Network Management10.1002/nem.2081Online publication date: 28-Nov-2019
https://doi.org/10.1002/nem.2081

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