###### Abstract  
The quality of experience (QoE) assessment of adaptive video streaming may be crucial for detecting degradations impacting customer satisfaction. In a telecommunication environment, eliminating failure points may be the highest priority. This study aims to assess the QoE level of the video played by the STB device connected to the production TV system. The evaluation has been based on the stalling effects, video quality changes, and the time related to the last decreased bitrate change occurrence. The two-phase continuous clustering approach has been studied to assess the QoE level based on the ACR scale. The number of devices with grades 1 or 2 is relatively low, but those devices generate significantly more events than adequately functioning devices. STBs try to play the highest possible bitrate, and there is no possibility of setting the intermediate bitrate level. The STB player does not have the button to set the quality level, usually available in pure over-the-top applications. Hence the bitrate fluctuations that can annoy customers appear for the lowest grades. The boundary cases can be easily assessed. The outcome should be challenged by the customers' opinions to find the proper QoE threshold. Continuous clustering may allow telecom operators to assess customer satisfaction with their TV service. artificial intelligence; unsupervised learning; clustering; quality of experience; adaptive streaming; over-the-top +
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for adaptive video streaming are stalls, video quality changes, and initial delay [3]. The stallings are connected to buffer saturation and, as a result, a black screen or rebuffering may occur. Video quality changes are associated with changing the level of video bitrate. The higher bitrate, the better the video quality. The video player can decrease the bitrate level during network impairments when the bandwidth is insufficient. Hence the video will still be played. The initial delay is related to the time needed to fill the player buffer above the specified threshold that allows starting playback. In pure OTT applications, the initial delay may also be connected to commercials played before the relevant video starts. However, the initial delay might not be as harmful as stalling or bitrate change. It was stated that the occurrence of stalling might be six times more significant than the initial delay in leaving the video [4]. Several studies on the QoE in adaptive video streaming environments have been conducted. The k-means clustering algorithm and classification approach was used to assess whether the viewer may experience a positive or negative QoE level. Mainly stallings, bitrate changes, initial delay, and duration metrics were considered. The proposed algorithm has a precision of over 96%. The study covers batch clustering with a classification of the data collected from mobile networks [5]. The video ATLAS algorithm based on a support vector regressor (SVR) can assess the QoE level based on video quality, the occurrence of stallings as well as memory-related to the last distortion interval. However, it is limited to subjective data used for algorithm training [6]. Reinforcement learning was used to increase the adaptive video streaming QoE level in mobile devices used in transport. The quality was measured on a tram, ferry, and bus. TV operators can consider QoE measurements for mobile devices since mobile TV services are often complementary to regular services [7]. The prediction approach named Streaming QoE Index considers the quality degradation due to perceptual video impairment and stallings, initial buffering, and interactions between them. The study indicates that video content coded with a constant bitrate could have different presentation qualities that might influence the QoE level [8]. Adaptive streaming QoE evaluation algorithms based on calculated peak to signal noise ratio (PSNR), video multimethod assessment fusion (VMAF), and bitrate have been proposed to consider the encoding quality, rebufferings, quality changes, and initial delays.