Unsupervised Learning Model for Finding Correlations Between Network and Percieved QoS
During delivery of video service, most of management control systems are able to collect useful data from different OSI layers that help in indication quality of video service. Recently, a mathematical model has been proposed which, only with a help of data collected at PHY (physical) and MAC (Media Access Control) layers and after appearance of certain degradations in transmission channel, estimates QoS (Quality of Service) indicators and then objective QoE (Quality of Experience). In this paper, we analyse that mathematical model, but only in the case of sudden occurrence of significant disturbance in transmission channel. In addition, we have chosen AIC criterion instead of the vector one in order to define the size of L-value. Validation and verification of the model are done in DSL (Digital Subscriber Line) environment during IPTV (Internet Protocol Television) service delivering and NS2 respectively.