Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications
Video streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/14/9/268 |
| _version_ | 1797488201246441472 |
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| author | Majd Nafeh Arash Bozorgchenani Daniele Tarchi |
| author_facet | Majd Nafeh Arash Bozorgchenani Daniele Tarchi |
| author_sort | Majd Nafeh |
| collection | DOAJ |
| description | Video streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-tourism applications, to name a few. Although the majority of the implemented solutions resort to a centralized cloud-based approach, the interest in edge/fog-based approaches is increasing. Fog-based VoD services result in fulfilling the stringent low-latency requirement of 5G and B5G networks. In the following, by resorting to the Dynamic Adaptive Streaming over HTTP (DASH) technique, we design a video-segment deployment algorithm for streaming services in a fog computing environment. In particular, by exploiting the inherent adaptation of the DASH approach, we embed in the system a joint transcoding and scalable video coding (SVC) approach able to deploy at run-time the video segments upon the user’s request. With this in mind, two algorithms have been developed aiming at maximizing the marginal gain with respect to a pre-defined delay threshold and enabling video quality downgrade for faster video deployment. Numerical results demonstrate that by effectively mapping the video segments, it is possible to minimize the streaming latency while maximising the users’ target video quality. |
| first_indexed | 2024-03-09T23:58:43Z |
| format | Article |
| id | doaj.art-bef28bdb53a7415583ba1128d8419957 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T23:58:43Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-bef28bdb53a7415583ba1128d84199572023-11-23T16:20:51ZengMDPI AGFuture Internet1999-59032022-09-0114926810.3390/fi14090268Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video ApplicationsMajd Nafeh0Arash Bozorgchenani1Daniele Tarchi2Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, 40121 Bologna, ItalySchool of Computing and Communications, Lancaster University, Lancaster LA1 4YQ, UKDepartment of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, 40121 Bologna, ItalyVideo streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-tourism applications, to name a few. Although the majority of the implemented solutions resort to a centralized cloud-based approach, the interest in edge/fog-based approaches is increasing. Fog-based VoD services result in fulfilling the stringent low-latency requirement of 5G and B5G networks. In the following, by resorting to the Dynamic Adaptive Streaming over HTTP (DASH) technique, we design a video-segment deployment algorithm for streaming services in a fog computing environment. In particular, by exploiting the inherent adaptation of the DASH approach, we embed in the system a joint transcoding and scalable video coding (SVC) approach able to deploy at run-time the video segments upon the user’s request. With this in mind, two algorithms have been developed aiming at maximizing the marginal gain with respect to a pre-defined delay threshold and enabling video quality downgrade for faster video deployment. Numerical results demonstrate that by effectively mapping the video segments, it is possible to minimize the streaming latency while maximising the users’ target video quality.https://www.mdpi.com/1999-5903/14/9/268fog computingDASHscalable video codingtranscoding |
| spellingShingle | Majd Nafeh Arash Bozorgchenani Daniele Tarchi Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications Future Internet fog computing DASH scalable video coding transcoding |
| title | Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications |
| title_full | Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications |
| title_fullStr | Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications |
| title_full_unstemmed | Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications |
| title_short | Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications |
| title_sort | joint scalable video coding and transcoding solutions for fog computing assisted dash video applications |
| topic | fog computing DASH scalable video coding transcoding |
| url | https://www.mdpi.com/1999-5903/14/9/268 |
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