A generic quantitative relationship between quality of experience and quality of service
Quality of experience ties together user perception, experience, and expectations to application and network performance, typically expressed by quality of service parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms on...
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| Veröffentlicht in: | IEEE network 2010-03, Vol.24 (2), p.36-41 |
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| creator | Fiedler, M. Hossfeld, T. Phuoc Tran-Gia |
| description | Quality of experience ties together user perception, experience, and expectations to application and network performance, typically expressed by quality of service parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. Against this background, this article proposes a generic formula in which QoE and QoS parameters are connected through an exponential relationship, called IQX hypothesis. The formula relates changes of QoE with respect to QoS to the current level of QoE, is simple to match, and its limit behaviors are straightforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores is expressed as functions of loss and reordering ratio, the latter of which is caused by jitter. For web surfing as the second application area, matchings provided by the IQX hypothesis are shown to outperform previously published logarithmic functions. We conclude that the IQX hypothesis is a strong candidate to be taken into account when deriving relationships between QoE and QoS parameters. |
| doi_str_mv | 10.1109/MNET.2010.5430142 |
| format | Article |
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Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. Against this background, this article proposes a generic formula in which QoE and QoS parameters are connected through an exponential relationship, called IQX hypothesis. The formula relates changes of QoE with respect to QoS to the current level of QoE, is simple to match, and its limit behaviors are straightforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores is expressed as functions of loss and reordering ratio, the latter of which is caused by jitter. For web surfing as the second application area, matchings provided by the IQX hypothesis are shown to outperform previously published logarithmic functions. We conclude that the IQX hypothesis is a strong candidate to be taken into account when deriving relationships between QoE and QoS parameters.</description><subject>cancellation rate</subject><subject>Construction</subject><subject>Delay effects</subject><subject>differential equation</subject><subject>Distortion measurement</subject><subject>Full reference metric</subject><subject>Image quality</subject><subject>IQX hypothesis</subject><subject>Jitter</subject><subject>Laboratories</subject><subject>Matching</subject><subject>Monitoring</subject><subject>MOS</subject><subject>Networks</subject><subject>no reference metric</subject><subject>Perception</subject><subject>PESQ</subject><subject>Quality of service</subject><subject>reduced reference metric</subject><subject>response time</subject><subject>Testing</subject><subject>Throughput</subject><subject>Timing</subject><subject>user rating</subject><issn>0890-8044</issn><issn>1558-156X</issn><issn>1558-156X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>D8T</sourceid><recordid>eNpNkDtPwzAUhS0EEqXwAxBLNgaU4kdcJ2NVykMqsBTUzYqTm9YoTVLbaem_x1GgYrqv7xxdHYSuCR4RgpP717fZYkSxH3nEMInoCRoQzuOQ8PHyFA1wnOAwxlF0ji6s_cIe4YwO0HISrKACo7Ng26aV0y51egeBgdI3dWXXugkUuD1A1RGldoegLgL4brwIqgyCtMr_XyyYnc7gEp0VaWnh6rcO0cfjbDF9DufvTy_TyTzMogS7kMdUYEqBCcITFgmcCJUzwYsxV0TFjMQ5URQ48d9yLDK_AcaKRME4x0wJNkR3va_dQ9Mq2Ri9Sc1B1qmWD_pzImuzksqtpRAJ8fRtTzem3rZgndxom0FZphXUrZWCM0HHHDNPkp7MTG2tgeLoTLDsIpdd5LKLXP5G7jU3vUYDwJH_u_4ACXR9Ow</recordid><startdate>20100301</startdate><enddate>20100301</enddate><creator>Fiedler, M.</creator><creator>Hossfeld, T.</creator><creator>Phuoc Tran-Gia</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>L7M</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8T</scope><scope>DF3</scope><scope>ZZAVC</scope></search><sort><creationdate>20100301</creationdate><title>A generic quantitative relationship between quality of experience and quality of service</title><author>Fiedler, M. ; Hossfeld, T. ; Phuoc Tran-Gia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c490t-5827022e37159347097bd375f65b1b8318d1b2e51532507c831e33f9be6d03b73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>cancellation rate</topic><topic>Construction</topic><topic>Delay effects</topic><topic>differential equation</topic><topic>Distortion measurement</topic><topic>Full reference metric</topic><topic>Image quality</topic><topic>IQX hypothesis</topic><topic>Jitter</topic><topic>Laboratories</topic><topic>Matching</topic><topic>Monitoring</topic><topic>MOS</topic><topic>Networks</topic><topic>no reference metric</topic><topic>Perception</topic><topic>PESQ</topic><topic>Quality of service</topic><topic>reduced reference metric</topic><topic>response time</topic><topic>Testing</topic><topic>Throughput</topic><topic>Timing</topic><topic>user rating</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fiedler, M.</creatorcontrib><creatorcontrib>Hossfeld, T.</creatorcontrib><creatorcontrib>Phuoc Tran-Gia</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SWEPUB Blekinge Tekniska Högskola</collection><collection>SwePub Articles full text</collection><jtitle>IEEE network</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fiedler, M.</au><au>Hossfeld, T.</au><au>Phuoc Tran-Gia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A generic quantitative relationship between quality of experience and quality of service</atitle><jtitle>IEEE network</jtitle><stitle>NET-M</stitle><date>2010-03-01</date><risdate>2010</risdate><volume>24</volume><issue>2</issue><spage>36</spage><epage>41</epage><pages>36-41</pages><issn>0890-8044</issn><issn>1558-156X</issn><eissn>1558-156X</eissn><coden>IENEET</coden><abstract>Quality of experience ties together user perception, experience, and expectations to application and network performance, typically expressed by quality of service parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. Against this background, this article proposes a generic formula in which QoE and QoS parameters are connected through an exponential relationship, called IQX hypothesis. The formula relates changes of QoE with respect to QoS to the current level of QoE, is simple to match, and its limit behaviors are straightforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores is expressed as functions of loss and reordering ratio, the latter of which is caused by jitter. For web surfing as the second application area, matchings provided by the IQX hypothesis are shown to outperform previously published logarithmic functions. We conclude that the IQX hypothesis is a strong candidate to be taken into account when deriving relationships between QoE and QoS parameters.</abstract><pub>IEEE</pub><doi>10.1109/MNET.2010.5430142</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | cancellation rate Construction Delay effects differential equation Distortion measurement Full reference metric Image quality IQX hypothesis Jitter Laboratories Matching Monitoring MOS Networks no reference metric Perception PESQ Quality of service reduced reference metric response time Testing Throughput Timing user rating |
| title | A generic quantitative relationship between quality of experience and quality of service |
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