Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Continuous Video Quality of Experience Modelling using Machine Learning Model Trees
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
1996 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Adaptive video streaming is perpetually influenced by unpredictable network conditions, whichcauses playback interruptions like stalling, rebuffering and video bit rate fluctuations. Thisleads to potential degradation of end-user Quality of Experience (QoE) and may make userchurn from the service. Video QoE modelling that precisely predicts the end users QoE underthese unstable conditions is taken into consideration quickly. The root cause analysis for thesedegradations is required for the service provider. These sudden changes in trend are not visiblefrom monitoring the data from the underlying network service. Thus, this is challenging toknow this change and model the instantaneous QoE. For this modelling continuous time, QoEratings are taken into consideration rather than the overall end QoE rating per video. To reducethe user risk of churning the network providers should give the best quality to the users.

In this thesis, we proposed the QoE modelling to analyze the user reactions change over timeusing machine learning models. The machine learning models are used to predict the QoEratings and change patterns in ratings. We test the model on video Quality dataset availablepublicly which contains the user subjective QoE ratings for the network distortions. M5P modeltree algorithm is used for the prediction of user ratings over time. M5P model gives themathematical equations and leads to more insights by given equations. Results of the algorithmshow that model tree is a good approach for the prediction of the continuous QoE and to detectchange points of ratings. It is shown that to which extent these algorithms are used to estimatechanges. The analysis of model provides valuable insights by analyzing exponential transitionsbetween different level of predicted ratings. The outcome provided by the analysis explains theuser behavior when the quality decreases the user ratings decrease faster than the increase inquality with time. The earlier work on the exponential transitions of instantaneous QoE overtime is supported by the model tree to the user reaction to sudden changes such as video freezes.

Place, publisher, year, edition, pages
1996. , p. 39
Keywords [en]
QoE, Root cause analysis, User reactions over time, M5P Algorithm, Machine learning model, Continuous Video QoE, Model trees.
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:bth-17814OAI: oai:DiVA.org:bth-17814DiVA, id: diva2:1304719
Subject / course
ET2580 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Telecommunication Systems
Educational program
ETATX Master of Science Programme in Electrical Engineering with emphasis on Telecommunication Systems
Presentation
2019-01-28, J3208, Blekinge Tekniska Högskola, Karlskrona, 11:20 (English)
Supervisors
Examiners
Available from: 2019-04-15 Created: 2019-04-12 Last updated: 2019-04-15Bibliographically approved

Open Access in DiVA

BTH2019Chapala(1480 kB)29 downloads
File information
File name FULLTEXT02.pdfFile size 1480 kBChecksum SHA-512
b43d34a3eb0315c6767d28ac4678b09f3be58a18d4775bc0614abe8fb6c58b431338531575cfa1095ec05f11c46c4b188eb0693d52acbc88cf622ad7ff14a922
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Chapala, Usha KiranPeteti, Sridhar
By organisation
Department of Computer Science
Telecommunications

Search outside of DiVA

GoogleGoogle Scholar
Total: 29 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 177 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf