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

Alberto Testolin, Marco Zanforlin, Michele De Filippo De Grazia, Daniele Munaretto, Andrea Zanella, Marco Zorzi, Michele Zorzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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) algorithms. More specifically, we develop a learning system that can automatically extract the quality-rate characteristics of unknown video sequences from the size of H.264-encoded video frames. Our approach combines unsupervised feature learning with supervised classification techniques, thereby providing an efficient and scalable way to estimate the QoE parameters that characterize each video. This QoE characterization is then used to manage simultaneous video transmissions through a shared channel in order to guarantee a minimum quality level to the final users. Simulation results show that the proposed learning-based QoE classification of video sequences outperforms commonly deployed off-line video analysis techniques and that the QoE-based VAC and RM algorithms outperform standard content-agnostic strategies.

Original languageEnglish
Title of host publication2014 13th Annual Mediterranean Ad Hoc Networking Workshop, MED-HOC-NET 2014
PublisherIEEE Computer Society
Pages31-38
Number of pages8
ISBN (Print)9781479952588
DOIs
Publication statusPublished - 2014
Event2014 13th Annual Mediterranean Ad Hoc Networking Workshop, MED-HOC-NET 2014 - Piran, Slovenia
Duration: Jun 2 2014Jun 4 2014

Other

Other2014 13th Annual Mediterranean Ad Hoc Networking Workshop, MED-HOC-NET 2014
CountrySlovenia
CityPiran
Period6/2/146/4/14

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ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Testolin, A., Zanforlin, M., De Grazia, M. D. F., Munaretto, D., Zanella, A., Zorzi, M., & Zorzi, M. (2014). A machine learning approach to QoE-based video admission control and resource allocation in wireless systems. In 2014 13th Annual Mediterranean Ad Hoc Networking Workshop, MED-HOC-NET 2014 (pp. 31-38). [6849102] IEEE Computer Society. https://doi.org/10.1109/MedHocNet.2014.6849102