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Unsupervised learning data-driven continuous QoE assessment in adaptive streaming-based television system


[ 1 ] Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Instytut Sieci Teleinformatycznych, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ DW ] applied doctorate phd student | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication


Published in

Applied Sciences

Journal year: 2022 | Journal volume: vol. 12 | Journal number: iss. 16

Article type

scientific article

Publication language


  • artificial intelligence
  • unsupervised learning
  • clustering
  • quality of experience
  • adaptive streaming
  • over-the-top

EN 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.

Pages (from - to)

8288-1 - 8288-23


License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

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