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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Deep learning-based QoE prediction for streaming services in mobile networks

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      Author(s)
      Huang, Gan
      Erçetin, Özgür
      Gökcesu, Hakan
      Kalem, Gökhan
      Date
      2022-11-15
      Source Title
      International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)
      Electronic ISSN
      2160-4894
      Publisher
      IEEE
      Pages
      1 - 6
      Language
      English
      Type
      Conference Paper
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      Abstract
      Video streaming accounts for the most of the global Internet traffic and providing a high user Quality of Experience (QoE) is considered an essential target for mobile network operators (MNOs). QoE strongly depends on network Quality of Service (QoS) parameters. In this work, we use real-world network traces obtained from a major cellular operator in Turkey to establish a mapping from network side parameters to the user QoE. To this end, we use a model-aided deep learning method for first predicting channel path loss, and then, employ this prediction for predicting video streaming MOS. The experimental results demonstrate that the proposed model-aided deep learning model can guarantee higher prediction accuracy compared to predictions only relying on mathematical models. We also demonstrate that even though a trained model cannot be directly transferred from one geographical area to another, they significantly reduce the volume of required training when used for prediction in a new area.
      Keywords
      Quality of experience
      Prediction
      Deep learning
      Video streaming
      Mobile networks
      Key performance indicators
      Permalink
      http://hdl.handle.net/11693/111471
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/WiMob55322.2022.9941672
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      • Department of Electrical and Electronics Engineering 4011
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