A machine learning approach to QoE-based video admission control and resource allocation in wireless systems
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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.
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Testolin et al. [443] report a RMSE of below 3% using videos from a pool of 38 video clips with different data rates and durations.
“The authors report a RMSE of below 3% using videos from a pool of 38 video clips with different data rates and durations.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [443]
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- Authors
- Testolin, A., Zanforlin, M., De Filippo De Grazia, M., Munaretto, D., Zanella, A., Zorzi, M., & Zorzi, M.
- Affiliations
- University of Padua (IT)
- Venue
- 2014 13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET)
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 31-38
- OpenAlex
- W2107330855
- Cited by
- 45
- References
- 24
- Citations by year
- 2023: 3 · 2022: 3 · 2021: 2 · 2020: 7 · 2019: 7 · 2018: 5 · 2017: 8 · 2016: 4 · 2015: 5 · 2014: 1
- FWCI
- 3.23
- Type
- conference paper
- Language
- en
- Open access
- green
- ieeexplore.ieee.org
- Licence
- other-oa
- Locations
- Annual Mediterranean Ad Hoc Networking Workshop (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, other-oa)
- Funders
- Fondazione Cassa di Risparmio di Padova e Rovigo
- Army Research Office
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Keywords
- resource allocation, quality of experience, QoE prediction, H.264 video, video traffic, cellular networks, unsupervised feature learning, supervised classification, shared channel, machine learning
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