Multi-Resolution Multimedia QoE Models for IPTV Applications
Internet television (IPTV) is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to proactively deliver optimum user quality of experience (QoE) for IPTV, service providers need to identify network bottlenecks in real time. In this paper, w...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | International Journal of Digital Multimedia Broadcasting |
| Online Access: | http://dx.doi.org/10.1155/2012/904072 |
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| _version_ | 1832563944028372992 |
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| author | Prasad Calyam Prashanth Chandrasekaran Gregg Trueb Nathan Howes Rajiv Ramnath Delei Yu Ying Liu Lixia Xiong Daoyan Yang |
| author_facet | Prasad Calyam Prashanth Chandrasekaran Gregg Trueb Nathan Howes Rajiv Ramnath Delei Yu Ying Liu Lixia Xiong Daoyan Yang |
| author_sort | Prasad Calyam |
| collection | DOAJ |
| description | Internet television (IPTV) is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to proactively deliver optimum user quality of experience (QoE) for IPTV, service providers need to identify network bottlenecks in real time. In this paper, we develop psycho-acoustic-visual models that can predict user QoE of multimedia applications in real time based on online network status measurements. Our models are neural network based and cater to multi-resolution IPTV applications that include QCIF, QVGA, SD, and HD resolutions encoded using popular audio and video codec combinations. On the network side, our models account for jitter and loss levels, as well as router queuing disciplines: packet-ordered and time-ordered FIFO. We evaluate the performance of our multi-resolution multimedia QoE models in terms of prediction characteristics, accuracy, speed, and consistency. Our evaluation results demonstrate that the models are pertinent for real-time QoE monitoring and resource adaptation in IPTV content delivery networks. |
| format | Article |
| id | doaj-art-d80431f13f7140b39d843f2f1c191d8b |
| institution | Kabale University |
| issn | 1687-7578 1687-7586 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Digital Multimedia Broadcasting |
| spelling | doaj-art-d80431f13f7140b39d843f2f1c191d8b2025-02-03T01:12:15ZengWileyInternational Journal of Digital Multimedia Broadcasting1687-75781687-75862012-01-01201210.1155/2012/904072904072Multi-Resolution Multimedia QoE Models for IPTV ApplicationsPrasad Calyam0Prashanth Chandrasekaran1Gregg Trueb2Nathan Howes3Rajiv Ramnath4Delei Yu5Ying Liu6Lixia Xiong7Daoyan Yang8Ohio Supercomputer Center/OARnet, The Ohio State University, Columbus, OH 43210, USAOhio Supercomputer Center/OARnet, The Ohio State University, Columbus, OH 43210, USAOhio Supercomputer Center/OARnet, The Ohio State University, Columbus, OH 43210, USAOhio Supercomputer Center/OARnet, The Ohio State University, Columbus, OH 43210, USAOhio Supercomputer Center/OARnet, The Ohio State University, Columbus, OH 43210, USAHuawei Technologies, Shenzhen 518129, ChinaHuawei Technologies, Shenzhen 518129, ChinaHuawei Technologies, Shenzhen 518129, ChinaHuawei Technologies, Shenzhen 518129, ChinaInternet television (IPTV) is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to proactively deliver optimum user quality of experience (QoE) for IPTV, service providers need to identify network bottlenecks in real time. In this paper, we develop psycho-acoustic-visual models that can predict user QoE of multimedia applications in real time based on online network status measurements. Our models are neural network based and cater to multi-resolution IPTV applications that include QCIF, QVGA, SD, and HD resolutions encoded using popular audio and video codec combinations. On the network side, our models account for jitter and loss levels, as well as router queuing disciplines: packet-ordered and time-ordered FIFO. We evaluate the performance of our multi-resolution multimedia QoE models in terms of prediction characteristics, accuracy, speed, and consistency. Our evaluation results demonstrate that the models are pertinent for real-time QoE monitoring and resource adaptation in IPTV content delivery networks.http://dx.doi.org/10.1155/2012/904072 |
| spellingShingle | Prasad Calyam Prashanth Chandrasekaran Gregg Trueb Nathan Howes Rajiv Ramnath Delei Yu Ying Liu Lixia Xiong Daoyan Yang Multi-Resolution Multimedia QoE Models for IPTV Applications International Journal of Digital Multimedia Broadcasting |
| title | Multi-Resolution Multimedia QoE Models for IPTV Applications |
| title_full | Multi-Resolution Multimedia QoE Models for IPTV Applications |
| title_fullStr | Multi-Resolution Multimedia QoE Models for IPTV Applications |
| title_full_unstemmed | Multi-Resolution Multimedia QoE Models for IPTV Applications |
| title_short | Multi-Resolution Multimedia QoE Models for IPTV Applications |
| title_sort | multi resolution multimedia qoe models for iptv applications |
| url | http://dx.doi.org/10.1155/2012/904072 |
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