A machine learning approach to QoE-based video admission control and resource allocation in wireless systems

@article{Testolin2014AML,
  title={A machine learning approach to QoE-based video admission control and resource allocation in wireless systems},
  author={Alberto Testolin and Marco Zanforlin and Michele De Filippo De Grazia and Daniele Munaretto and Andrea Zanella and Marco Zorzi and Michele Zorzi},
  journal={2014 13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET)},
  year={2014},
  pages={31-38}
}
The rapid growth of video traffic in cellular networks is a crucial issue to be addressed by mobile operators. An emerging and promising trend in this regard is the development of solutions that aim at maximizing the Quality of Experience (QoE) of the end users. However, predicting the QoE perceived by the users in different conditions remains a major challenge. In this paper, we propose a machine learning approach to support QoE-based Video Admission Control (VAC) and Resource Management (RM… CONTINUE READING

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