A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing
With the development of information technology, more and more people use multimedia conference system to communicate or work across regions. In this article, an ultra-reliable and low-latency solution based on Deep Learning and assisted by Cloud Computing for multimedia conference system, called UCC...
Gespeichert in:
Veröffentlicht in: | International journal of information technology and web engineering 2018-07, Vol.13 (3), p.85-98 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 98 |
---|---|
container_issue | 3 |
container_start_page | 85 |
container_title | International journal of information technology and web engineering |
container_volume | 13 |
creator | Zhang, Wei Shi, Huiling Lu, Xinming Zhou, Longquan |
description | With the development of information technology, more and more people use multimedia conference system to communicate or work across regions. In this article, an ultra-reliable and low-latency solution based on Deep Learning and assisted by Cloud Computing for multimedia conference system, called UCCMCS, is designed and implemented. In UCCMCS, there are two-tiers in its data distribution structure which combines the advantages of cloud computing. And according to the requirements of ultra-reliability and low-latency, a bandwidth optimization model is proposed to improve the transmission efficiency of multimedia data so as to reduce the delay of the system. In order to improve the reliability of data distribution, the help of cloud computing node is used to carry out the retransmission of lost data. the experimental results show UCCMCS could improve the reliability and reduce the latency of the multimedia data distribution in multimedia conference system. |
doi_str_mv | 10.4018/IJITWE.2018070106 |
format | Article |
fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_gale_infotracmisc_A759603313</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A759603313</galeid><sourcerecordid>A759603313</sourcerecordid><originalsourceid>FETCH-LOGICAL-c405t-5e09369131a30535321d81333a2f4a76eb308ddeb7274d9c32f95f44966f1d083</originalsourceid><addsrcrecordid>eNp1UU1LAzEUDKKgVn-At4Dn1mST7Mex1I9WKh5s8RjSzUuJ7CZrsnvovzdStQhKDhkeM28eMwhdUTLhhJY3i8fF6vVukiVMCkJJfoTOqBB8TIlgxz-Yi1N0HuMbIZwzRs7QeopvATq8BBWcdVv84puht95h4wN-GpretqCtwjPvDARwNeCXXeyhxdMYbQIab3Z41vhBJ07bJbHbXqATo5oIl1__CK3v71az-Xj5_LCYTZfjmhPRjwWQiuUVZVSxdKZgGdUlZYypzHBV5LBhpNQaNkVWcF3VLDOVMJxXeW6oJiUboev93i749wFiL9_8EFyylFnFaMmrohAH1lY1IK0zvg-qbm2s5bQQVU4YS6YjNPmDlZ6G1tbegbFp_ktA94I6-BgDGNkF26qwk5TIz1LkvhR5KCVp5nuN3drDrdDJ7_zld_4y5f__omT_AdfnlHg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2931849775</pqid></control><display><type>article</type><title>A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing</title><source>ProQuest Central UK/Ireland</source><source>ProQuest Central</source><creator>Zhang, Wei ; Shi, Huiling ; Lu, Xinming ; Zhou, Longquan</creator><creatorcontrib>Zhang, Wei ; Shi, Huiling ; Lu, Xinming ; Zhou, Longquan</creatorcontrib><description>With the development of information technology, more and more people use multimedia conference system to communicate or work across regions. In this article, an ultra-reliable and low-latency solution based on Deep Learning and assisted by Cloud Computing for multimedia conference system, called UCCMCS, is designed and implemented. In UCCMCS, there are two-tiers in its data distribution structure which combines the advantages of cloud computing. And according to the requirements of ultra-reliability and low-latency, a bandwidth optimization model is proposed to improve the transmission efficiency of multimedia data so as to reduce the delay of the system. In order to improve the reliability of data distribution, the help of cloud computing node is used to carry out the retransmission of lost data. the experimental results show UCCMCS could improve the reliability and reduce the latency of the multimedia data distribution in multimedia conference system.</description><identifier>ISSN: 1554-1045</identifier><identifier>EISSN: 1554-1053</identifier><identifier>DOI: 10.4018/IJITWE.2018070106</identifier><language>eng</language><publisher>Hershey: IGI Global</publisher><subject>Bandwidths ; Cloud computing ; Computer networks ; Computer science ; Conferencing systems ; Data integrity ; Deep learning ; Design ; Efficiency ; Engineering ; Information technology ; Internet ; Multimedia ; Optimization ; Optimization models ; Performance evaluation ; Reliability ; Streaming media ; Supercomputers ; Transmission efficiency</subject><ispartof>International journal of information technology and web engineering, 2018-07, Vol.13 (3), p.85-98</ispartof><rights>COPYRIGHT 2018 IGI Global</rights><rights>Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-5e09369131a30535321d81333a2f4a76eb308ddeb7274d9c32f95f44966f1d083</citedby><cites>FETCH-LOGICAL-c405t-5e09369131a30535321d81333a2f4a76eb308ddeb7274d9c32f95f44966f1d083</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2931849775?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,43805,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Zhang, Wei</creatorcontrib><creatorcontrib>Shi, Huiling</creatorcontrib><creatorcontrib>Lu, Xinming</creatorcontrib><creatorcontrib>Zhou, Longquan</creatorcontrib><title>A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing</title><title>International journal of information technology and web engineering</title><description>With the development of information technology, more and more people use multimedia conference system to communicate or work across regions. In this article, an ultra-reliable and low-latency solution based on Deep Learning and assisted by Cloud Computing for multimedia conference system, called UCCMCS, is designed and implemented. In UCCMCS, there are two-tiers in its data distribution structure which combines the advantages of cloud computing. And according to the requirements of ultra-reliability and low-latency, a bandwidth optimization model is proposed to improve the transmission efficiency of multimedia data so as to reduce the delay of the system. In order to improve the reliability of data distribution, the help of cloud computing node is used to carry out the retransmission of lost data. the experimental results show UCCMCS could improve the reliability and reduce the latency of the multimedia data distribution in multimedia conference system.</description><subject>Bandwidths</subject><subject>Cloud computing</subject><subject>Computer networks</subject><subject>Computer science</subject><subject>Conferencing systems</subject><subject>Data integrity</subject><subject>Deep learning</subject><subject>Design</subject><subject>Efficiency</subject><subject>Engineering</subject><subject>Information technology</subject><subject>Internet</subject><subject>Multimedia</subject><subject>Optimization</subject><subject>Optimization models</subject><subject>Performance evaluation</subject><subject>Reliability</subject><subject>Streaming media</subject><subject>Supercomputers</subject><subject>Transmission efficiency</subject><issn>1554-1045</issn><issn>1554-1053</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1UU1LAzEUDKKgVn-At4Dn1mST7Mex1I9WKh5s8RjSzUuJ7CZrsnvovzdStQhKDhkeM28eMwhdUTLhhJY3i8fF6vVukiVMCkJJfoTOqBB8TIlgxz-Yi1N0HuMbIZwzRs7QeopvATq8BBWcdVv84puht95h4wN-GpretqCtwjPvDARwNeCXXeyhxdMYbQIab3Z41vhBJ07bJbHbXqATo5oIl1__CK3v71az-Xj5_LCYTZfjmhPRjwWQiuUVZVSxdKZgGdUlZYypzHBV5LBhpNQaNkVWcF3VLDOVMJxXeW6oJiUboev93i749wFiL9_8EFyylFnFaMmrohAH1lY1IK0zvg-qbm2s5bQQVU4YS6YjNPmDlZ6G1tbegbFp_ktA94I6-BgDGNkF26qwk5TIz1LkvhR5KCVp5nuN3drDrdDJ7_zld_4y5f__omT_AdfnlHg</recordid><startdate>20180701</startdate><enddate>20180701</enddate><creator>Zhang, Wei</creator><creator>Shi, Huiling</creator><creator>Lu, Xinming</creator><creator>Zhou, Longquan</creator><general>IGI Global</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>20180701</creationdate><title>A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing</title><author>Zhang, Wei ; Shi, Huiling ; Lu, Xinming ; Zhou, Longquan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-5e09369131a30535321d81333a2f4a76eb308ddeb7274d9c32f95f44966f1d083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Bandwidths</topic><topic>Cloud computing</topic><topic>Computer networks</topic><topic>Computer science</topic><topic>Conferencing systems</topic><topic>Data integrity</topic><topic>Deep learning</topic><topic>Design</topic><topic>Efficiency</topic><topic>Engineering</topic><topic>Information technology</topic><topic>Internet</topic><topic>Multimedia</topic><topic>Optimization</topic><topic>Optimization models</topic><topic>Performance evaluation</topic><topic>Reliability</topic><topic>Streaming media</topic><topic>Supercomputers</topic><topic>Transmission efficiency</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Wei</creatorcontrib><creatorcontrib>Shi, Huiling</creatorcontrib><creatorcontrib>Lu, Xinming</creatorcontrib><creatorcontrib>Zhou, Longquan</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><jtitle>International journal of information technology and web engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Wei</au><au>Shi, Huiling</au><au>Lu, Xinming</au><au>Zhou, Longquan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing</atitle><jtitle>International journal of information technology and web engineering</jtitle><date>2018-07-01</date><risdate>2018</risdate><volume>13</volume><issue>3</issue><spage>85</spage><epage>98</epage><pages>85-98</pages><issn>1554-1045</issn><eissn>1554-1053</eissn><abstract>With the development of information technology, more and more people use multimedia conference system to communicate or work across regions. In this article, an ultra-reliable and low-latency solution based on Deep Learning and assisted by Cloud Computing for multimedia conference system, called UCCMCS, is designed and implemented. In UCCMCS, there are two-tiers in its data distribution structure which combines the advantages of cloud computing. And according to the requirements of ultra-reliability and low-latency, a bandwidth optimization model is proposed to improve the transmission efficiency of multimedia data so as to reduce the delay of the system. In order to improve the reliability of data distribution, the help of cloud computing node is used to carry out the retransmission of lost data. the experimental results show UCCMCS could improve the reliability and reduce the latency of the multimedia data distribution in multimedia conference system.</abstract><cop>Hershey</cop><pub>IGI Global</pub><doi>10.4018/IJITWE.2018070106</doi><tpages>14</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1554-1045 |
ispartof | International journal of information technology and web engineering, 2018-07, Vol.13 (3), p.85-98 |
issn | 1554-1045 1554-1053 |
language | eng |
recordid | cdi_gale_infotracmisc_A759603313 |
source | ProQuest Central UK/Ireland; ProQuest Central |
subjects | Bandwidths Cloud computing Computer networks Computer science Conferencing systems Data integrity Deep learning Design Efficiency Engineering Information technology Internet Multimedia Optimization Optimization models Performance evaluation Reliability Streaming media Supercomputers Transmission efficiency |
title | A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T12%3A48%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Deep%20Learning%20Solution%20for%20Multimedia%20Conference%20System%20Assisted%20by%20Cloud%20Computing&rft.jtitle=International%20journal%20of%20information%20technology%20and%20web%20engineering&rft.au=Zhang,%20Wei&rft.date=2018-07-01&rft.volume=13&rft.issue=3&rft.spage=85&rft.epage=98&rft.pages=85-98&rft.issn=1554-1045&rft.eissn=1554-1053&rft_id=info:doi/10.4018/IJITWE.2018070106&rft_dat=%3Cgale_proqu%3EA759603313%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2931849775&rft_id=info:pmid/&rft_galeid=A759603313&rfr_iscdi=true |