A Deep Reinforcement Learning Quality Optimization Framework for Multimedia Streaming over 5G Networks Article Swipe
Alberto del Río
,
Javier Serrano
,
David Jiménez
,
Luis M. Contreras
,
Federico Álvarez
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app122010343
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app122010343
Media applications are amongst the most demanding services. They require high amounts of network capacity as well as computational resources for synchronous high-quality audio–visual streaming. Recent technological advances in the domain of new generation networks, specifically network virtualization and Multiaccess Edge Computing (MEC) have unlocked the potential of the media industry. They enable high-quality media services through dynamic and efficient resource allocation taking advantage of the flexibility of the layered architecture offered by 5G. The presented work demonstrates the potential application of Artificial Intelligence (AI) capabilities for multimedia services deployment. The goal was targeted to optimize the Quality of Experience (QoE) of real-time video using dynamic predictions by means of Deep Reinforcement Learning (DRL) algorithms. Specifically, it contains the initial design and test of a self-optimized cloud streaming proof-of-concept. The environment is implemented through a virtualized end-to-end architecture for multimedia transmission, capable of adapting streaming bitrate based on a set of actions. A prediction algorithm is trained through different state conditions (QoE, bitrate, encoding quality, and RAM usage) that serves the optimizer as the encoding values of the environment for action prediction. Optimization is applied by selecting the most suitable option from a set of actions. These consist of a collection of predefined network profiles with associated bitrates, which are validated by a list of reward functions. The optimizer is built employing the most prominent algorithms in the DRL family, with the use of two Neural Networks (NN), named Advantage Actor–Critic (A2C). As a result of its application, the ratio of good quality video segments increased from 65% to 90%. Furthermore, the number of image artifacts is reduced compared to standard sessions without applying intelligent optimization. From these achievements, the global QoE obtained is clearly better. These results, based on a simulated scenario, increase the interest in further research on the potential of applying intelligence to enhance the provisioning of media services under real conditions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app122010343
- https://www.mdpi.com/2076-3417/12/20/10343/pdf?version=1666582672
- OA Status
- gold
- Cited By
- 14
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306362588
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4306362588Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app122010343Digital Object Identifier
- Title
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A Deep Reinforcement Learning Quality Optimization Framework for Multimedia Streaming over 5G NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-10-14Full publication date if available
- Authors
-
Alberto del Río, Javier Serrano, David Jiménez, Luis M. Contreras, Federico ÁlvarezList of authors in order
- Landing page
-
https://doi.org/10.3390/app122010343Publisher landing page
- PDF URL
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https://www.mdpi.com/2076-3417/12/20/10343/pdf?version=1666582672Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/12/20/10343/pdf?version=1666582672Direct OA link when available
- Concepts
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Computer science, Reinforcement learning, Quality of experience, Multimedia, Flexibility (engineering), Software deployment, Distributed computing, Quality of service, Computer network, Artificial intelligence, Operating system, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 5, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
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14Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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