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SMPTE Meetings and Conferences ( October 2023)
[ACTIVE]

Cloud-based Internet Linear Video Delivery System Using Edge Computing on CDN

Metadata

Publisher
SMPTE — White Plains, NY, USA
Doc Type
Conference Paper
Content Type
Original Research
Volume
2023, No. 10, pp. 14–17
Abstract
Internet users are increasingly exposed to linear streaming services. Unlike television (TV) broadcasting, there is no limit to the number of channels that can be delivered via linear streaming services. Considering regions and various viewer preferences, several linear delivery platforms on cloud are being developed to efficiently provide various channels. In such platforms that use HTTP adaptive streaming (HAS), programs are scheduled in a manipulated manifest file. However, when an urgent program is dynamically inserted, the aforementioned approach requires complex implementations on the server and player sides to ensure reliable and rapid program switching, especially in low latency delivery because all the players’ manifest files must be quickly updated. Thus, we propose a cloud-based HAS linear delivery system that utilizes edge computing within the content delivery network (CDN) to facilitate prompt schedule changes. By shifting away from manifest manipulation or baseband switching used in TV broadcasting systems, the proposed approach offers quick adaptations to urgent schedule changes and efficient delivery of addressable linear streaming channels. This study presents an end-to-end system that focuses on the efficient use of cloud resources for encoding, program switching at pre-scheduled times by leveraging the CDN's edge capabilities, and simple low-latency playback. Moreover, the prompt realization of program schedule changes, particularly in scenarios involving dynamic urgent program insertions, and the replacement of certain programs based on viewers’ attributes on CDN edge, are addressed. By utilizing CDN edge computing, the cloud-based system offers dependable program switching to efficiently deliver localized or personalized channels.
Publication Date
2023-10-16
DOI
10.5594/M002003
ISBN
Print: 978-1-61482-964-5
Link
https://doi.org/10.5594/M002003
Author(s)
Daiki FukudomeJapan Broadcasting Corporation (NHK)
Satoshi NishimuraJapan Broadcasting Corporation (NHK)
Keyword(s)
Linear Streaming, HAS, DASH, Common Media Application Format (CMAF), Content Delivery Network, CDN Edge Computing
Copyright
© 2023 Society of Motion Picture and Television Engineers, Inc.

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Daiki Fukudome and Satoshi Nishimura; Cloud-based Internet Linear Video Delivery System Using Edge Computing on CDN, SMPTE Meetings and Conferences ( October 2023); SMPTE, 2023, ISBN: 978-1-61482-964-5. Available at https://doi.org/10.5594/M002003
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Daiki Fukudome and Satoshi Nishimura; Cloud-based Internet Linear Video Delivery System Using Edge Computing on CDN, SMPTE Meetings and Conferences ( October 2023); SMPTE, 2023, ISBN: 978-1-61482-964-5. Available at https://doi.org/10.5594/M002003

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Daiki Fukudome and Satoshi Nishimura; Cloud-based Internet Linear Video Delivery System Using Edge Computing on CDN, SMPTE Meetings and Conferences ( October 2023); SMPTE, 2023, ISBN: 978-1-61482-964-5. Available at https://doi.org/10.5594/M002003
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<span class="citation">Daiki Fukudome and Satoshi Nishimura; <cite>Cloud-based Internet Linear Video Delivery System Using Edge Computing on CDN</cite>, SMPTE Meetings and Conferences ( October 2023); SMPTE, 2023, ISBN: 978-1-61482-964-5. Available at <a href="https://doi.org/10.5594/M002003" target="_blank" rel="noopener">https://doi.org/10.5594/M002003</a></span>

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Preview:
Daiki Fukudome and Satoshi Nishimura; Cloud-based Internet Linear Video Delivery System Using Edge Computing on CDN, SMPTE Meetings and Conferences ( October 2023); SMPTE, 2023, ISBN: 978-1-61482-964-5
doi: 10.5594/M002003
url: https://doi.org/10.5594/M002003
Snippet:
<li>
Daiki Fukudome and Satoshi Nishimura; <cite id="bib-10-5594-m002003">Cloud-based Internet Linear Video Delivery System Using Edge Computing on CDN</cite>, SMPTE Meetings and Conferences ( October 2023); SMPTE, 2023, ISBN: 978-1-61482-964-5
<span class="doi">10.5594/M002003</span>
</li>