research-article

Real-Time Load Reduction in Multimedia Big Data for Mobile Internet

Authors:

Der-Jiunn DengAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 12, Issue 5s

Article No.: 76, Pages 1 - 20

https://doi.org/10.1145/2990473

Published: 12 October 2016 Publication History

Abstract

In the age of multimedia big data, the popularity of mobile devices has been in an unprecedented growth, the speed of data increasing is faster than ever before, and Internet traffic is rapidly increasing, not only in volume but also in heterogeneity. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive papers have been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this article, a hybrid-stream model is proposed to solve these problems for video analysis. Functionality of this model covers Data Preprocessing, Data Classification, and Data-Load-Reduction Processing. Specifically, an improved Convolutional Neural Networks (CNN) classification algorithm is designed to evaluate the importance of each video frame and video clip to enhance classification precision. Then, a reliable keyframe extraction mechanism will recognize the importance of each frame or clip, and decide whether to abandon it automatically by a series of correlation operations. The model will reduce data load to a dynamic threshold changed by σ, control the input size of the video in mobile Internet, and thus reduce network overload. Through experimental simulations, we find that the size of processed video has been effectively reduced and the quality of experience (QoE) has not been lowered due to a suitably selected parameter η. The simulation also shows that the model has a steady performance and is powerful enough for continuously growing multimedia big data.

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Zhou ZOnireti OXu HZhang LImran M(2024)AI and Blockchain Enabled Future Wireless Networks: A Survey And OutlookDistributed Ledger Technologies: Research and Practice10.1145/36443693:3(1-30)Online publication date: 9-Sep-2024
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Index Terms

Real-Time Load Reduction in Multimedia Big Data for Mobile Internet
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
2. Networks
  1. Network algorithms
    1. Data path algorithms
  2. Network properties
    1. Network reliability

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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 12, Issue 5s

Special Section on Multimedia Big Data: Networking and Special Section on Best Papers From ACM MMSYS/NOSSDAV 2015

December 2016

288 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3001754

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Copyright © 2016 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: 12 October 2016

Accepted: 01 February 2016

Revised: 01 January 2016

Received: 01 December 2015

Published in TOMM Volume 12, Issue 5s

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Research-article
Research
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Funding Sources

Open Research Fund of Key Lab of Broadband Wireless Communication
Ministry of Education
Educational Commission of Guangdong Province
NSFC
SFDPH
Jiangsu Qing Lan Project
International and Hong Kong
NSF of Jiangsu Province
NUPT
Guangdong High-Tech Development Fund
Macao 8 Taiwan collaborative innovation platform and major international cooperation projects of colleges in Guangdong Province
2013 Top Level Talents Project in the Sailing Plan of Guangdong Province
Priority Academic Program Development of Jiangsu Higher Education Institutions
Sensor Network Technology (NUPT)
2014 Guangdong Province Outstanding Young Professor Project

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Cited By

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https://doi.org/10.4108/eetiot.6519
Zhou ZOnireti OXu HZhang LImran M(2024)AI and Blockchain Enabled Future Wireless Networks: A Survey And OutlookDistributed Ledger Technologies: Research and Practice10.1145/36443693:3(1-30)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3644369
Du ZPang XQian H(2023)PartitionChain: A Scalable and Reliable Data Storage Strategy for Permissioned BlockchainIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313655635:4(4124-4136)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2021.3136556
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Yang CXu XZhou XQi L(2022)Deep Q Network–Driven Task Offloading for Efficient Multimedia Data Analysis in Edge Computing–Assisted IoVACM Transactions on Multimedia Computing, Communications, and Applications10.1145/354868718:2s(1-24)Online publication date: 6-Oct-2022
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