Scalable virtual network video-optimizer for adaptive real-time video transmission in 5G networks
- Submitting institution
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University of the West of Scotland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14879258
- Type
- D - Journal article
- DOI
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10.1109/TNSM.2020.2978975
- Title of journal
- IEEE Transactions on Network and Service Management
- Article number
- -
- First page
- 1068
- Volume
- 17
- Issue
- 2
- ISSN
- 1932-4537
- Open access status
- Technical exception
- Month of publication
- March
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This contribution helps address the major challenge in 5G user plane introduced by video application traffic, which has dominated the global Internet and 4G/5G networks and is ever-growing. The solution leverages Network Function Virtualisation (NFV), a cornerstone technology in 5G, and kernel-space video processing to have achieved a highly scalable and thus practical video optimisation scheme capable of mitigating network congestion caused by video traffic without noticeably compromising the video delivery quality. This is a key enabler for various 5G video use cases, partially reported in SliceNet Deliverables D4.3 and D8.6 and deployed in Dell EMC’s testbed.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -