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Propagation-based social-aware multimedia content distribution

Published: 17 October 2013 Publication History

Abstract

Online social networks have reshaped how multimedia contents are generated, distributed, and consumed on today's Internet. Given the massive number of user-generated contents shared in online social networks, users are moving to directly access these contents in their preferred social network services. It is intriguing to study the service provision of social contents for global users with satisfactory quality of experience. In this article, we conduct large-scale measurement of a real-world online social network system to study the social content propagation. We have observed important propagation patterns, including social locality, geographical locality, and temporal locality. Motivated by the measurement insights, we propose a propagation-based social-aware delivery framework using a hybrid edge-cloud and peer-assisted architecture. We also design replication strategies for the architecture based on three propagation predictors designed by jointly considering user, content, and context information. In particular, we design a propagation region predictor and a global audience predictor to guide how the edge-cloud servers backup the contents, and a local audience predictor to guide how peers cache the contents for their friends. Our trace-driven experiments further demonstrate the effectiveness and superiority of our design.

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 9, Issue 1s
Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
October 2013
218 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/2523001
Issue’s Table of Contents
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: 17 October 2013
Accepted: 01 May 2013
Received: 01 February 2013
Published in TOMM Volume 9, Issue 1s

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  1. Social network
  2. video service

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  • (2019)T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer MobilityFuture Internet10.3390/fi1102002811:2(28)Online publication date: 27-Jan-2019
  • (2019)Toward Edge-Assisted Video Content Intelligent Caching With Long Short-Term Memory LearningIEEE Access10.1109/ACCESS.2019.29470677(152832-152846)Online publication date: 2019
  • (2019)Popularity-Based Video Caching Techniques for Cache-Enabled Networks: A SurveyIEEE Access10.1109/ACCESS.2019.28987347(27699-27719)Online publication date: 2019
  • (2018)Random Forest Exploiting Post-related and User-related Features for Social Media Popularity PredictionProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3266439(2013-2017)Online publication date: 15-Oct-2018
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