This thesis explored YouTube's performance when content is delivered via a newly released protocol QUIC (Quick UDP Internet Connection) in a native YouTube Android application. To do so, a previously developed system called YouQ was used and upgraded so that there is a choice between playing YouTube videos in a browser and in a native YouTube Android player. The options served to compare the behaviour of the two, which was in focus in the first part of the thesis. The results of the comparison showed that QUIC performs better than TCP (used in browser) in case when network conditions are sufficient to play videos in the highest available quality. YouTube relies on HTTP adaptive streaming, meaning that the quality changes automatically according to the situation on the client and in the network. Results of conducted experiments showed that when bandwidth decreases, YouTube Android player takes a longer time to adjust. Therefore, stalling events happen and if a video is starting, initial delay is notably longer than in case of a browser player. The second part of the thesis was to build machine learning models that could help network operators estimate Quality of Experience (QoE) despite the encrypted traffic. When calculating QoE, a model described in Recommendation ITU-T P.1203 (P.NATS) was respected. For the reason that YouTube Android API does not offer a method to note quality switches, only a quality impact due to buffering could be measured. The dataset that was analysed consisted of 428 instances. Classification models were created with WEKA machine learning tool and five algorithms: OneR, Logistic, SMO, Bagging and Random Forest. Classification into three and two possible classes was tried. The results showed that the most accurate model in both cases was created using Random Forest algorithm. Accuracy of 68% was achieved using three classes and of 83% using two classes. Moreover, in the scope of this thesis machine learning and WEKA tool were briefly explained as an introduction to the practical part. There is also an overview of QUIC protocol and P.NATS model. Finally, methodology, as well as all conducted experiments and collected data, were described in detail.