The video streaming applications contribute to a major share of the Internet traffic. Consequently, monitoring and management of video streaming quality has gained a significant importance in the recent years. The disturbances in the video, such as, amount of buffering and bitrate adaptations affect user Quality of Experience (QoE). Network operators usually monitor such events from network traffic with the help of Deep Packet Inspection (DPI). However, it is becoming difficult to monitor such events due to the traffic encryption. To address this challenge, this thesis work makes two key contributions. First, it presents a test-bed, which performs automated video streaming tests under controlled time-varying network conditions and measures performance at network and application level. Second, it develops and evaluates machine learning models for the detection of video buffering and bitrate adaptation events, which rely on the information extracted from packets headers. The findings of this work suggest that buffering and bitrate adaptation events within 60 second intervals can be detected using Random Forest model with an accuracy of about 70%. Moreover, the results show that the features based on time-varying patterns of downlink throughput and packet inter-arrival times play a distinctive role in the detection of such events.

The video streaming business has grown substantially during the last decades. To optimize the user experience in video streaming, it is important to know how the user satisfaction relates to the technical qualities for the video streaming services, such as bufferings and startup times.  The Net Promoter Score (NPS) is a widely used management tool used in surveys to measure customer satisfaction and loyalty. The users are categorized into three user groups based on a survey question. This thesis investigates whether it is possible to find correlations between the three user groups based on NPS ratings and measured technical qualities from video streams. Initial data exploring through information visualization suggested that the data should be separated into live streams and video-on-demand. Statistical analysis showed that the NPS user groups have no correlations to how long the users are watching the streams, nor to how long the video takes to start. The results showed, that the users watching live streams seem to be more sensitive to lower qualities than those watching video- on-demand. However, this could also be due to the fact that the measured technical qualities during live streams are generally lower. The buffering and the seek time proved to have correlations to the measured user satisfaction, but several other factors such as the actual video content could also have big impacts on the user’s ratings. The users which had experienced more buffering and longer seek times were more likely to rate the service with a lower score, than the average user. / Mängden videor som streamas över Internet har ökat väsentligt under de senaste årtiondena. För att kunna optimera upplevelsen för de som streamar videor är det viktigt för företagen som erbjuder dessa tjänster att veta hur kundnöjdheten relaterar till de tekniska egenskaperna. Dessa kan inkludera faktorer såsom buffring och starttider. Net Promoter Score (NPS) är ett verktyg som används inom många olika branscher för att mäta kundnöjdhet och lojalitet. Användarna delas in i tre grupper beroende på hur de svarar på en enkätfråga. Detta examensarbete undersöker huruvida det är möjligt att hitta korrelationer mellan kundnöjdhet baserad på de tre användargrupperna från NPS-verktyget och uppmätta tekniska kvaliteter från en streamingtjänst. Efter att inledande informationsvisualiseringar påvisade skillnader mellan live-strömmar och video-on-demand har dessa grupper hanterats separat. Statistisk analys visade att de tre NPS-grupperna inte har några korrelationer med starttiden eller hur länge användarna tittar på videor. Resultatet visade även att användarna som tittar på live-strömmar verkar vara känsligare för lägre tekniska kvaliteter än de som tittar på video-on-demand. Detta kan dock även bero på att de uppmätta tekniska egenskaperna under live-strömmarna generellt är lite lägre. Buffringen och söktiden visade sig ha samband med den uppmätta kundnöjdheten, men flera andra faktorer, såsom det faktiska videoinnehållet, kan också ha en inverkan på användarnas betyg. Användarna som hade upplevt mer buffring och längre söktider var mer benägna att ge tjänsten ett lägre betyg än den genomsnittliga användaren.