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.