The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced
learning. However, the COVID-19 containment measures that
forced people to work or stay at home, have determined a
significant increase in the Internet traffic that puts tremendous
pressure on the underlying network infrastructure. This affects
negatively content delivery and consequently user perceived
quality, especially for video-based services. Focusing on this
problem, this paper proposes a machine learning-based resource
allocation solution that improves the quality of video services
for increased number of viewers. The solution is deployed and
tested in an educational context, demonstrating its benefit in
terms of major quality of service parameters for various video
content, in comparison with existing state of the art. Moreover, a
discussion on how the technology is helping to mitigate the effects
of massively increasing internet traffic on the video quality in an
educational context is also presented.
Metadata
Item Type:
Article (Published)
Refereed:
Yes
Uncontrolled Keywords:
Video quality; machine learning; resource allocation; quality of service; technology enhanced learning