MIJ 2026, Volume 135, Number 2 (pp. 42 to 49)
[ACTIVE]
Integrating a Stream Transformation Engine in the Distribution Pipeline for Next-Gen Streaming Efficiency
Metadata
- Publisher
- SMPTE
- Doc Type
- Journal Article
- Content Type
- Original Research
- Abbreviated Title
- SMPTE Motion Imaging Jour.
- Volume
- 135, No. 2, pp. 42–49
- Abstract
- This paper proposes a distributed stream transformation architecture that integrates processing capabilities directly into IP-based media transport networks. As media distribution has transitioned from satellite to IP, solutions have been split between dedicated hardware and cloud platforms, each with scalability limitations. Our approach introduces a hardware-agnostic transformation framework supporting graphics processing unit (GPU), vision processing unit (VPU) (field-programmable gate array/application-specific integrated circuit (FPGA/ASIC)), and central processing unit (CPU) accelerators through unified Ku-bernetes orchestration, employing automatic repeat request (ARQ)-based protocols—Reliable Internet Stream Transport (RIST) and Secure Reliable Transport (SRT)—for reliable delivery over unmanaged networks. The architecture features a failure-tolerant control plane separated from the transport layer and a unified transformation engine running across high-performance core nodes and resource-constrained edge devices. Composable transformation pipelines eliminate duplicate processing by collocating multiple output requirements on optimal nodes. Cost analysis demonstrates that distributed commercial off-the-shelf (COTS) edge processing reduces per-stream costs from approximately ${\$}$ 281/month to ${\$}$ 97/month compared to managed cloud services over 36 months, supporting a hybrid deployment model.
- Publication Date
- 2026-04-01
- DOI
10.5594/JMI.2026/YPFV5033- ISSN
- Print:
1545-0279| Electronic:2160-2492 - Link
- https://doi.org/10.5594/JMI.2026/YPFV5033
- Author(s)
- Pierre Le FevreAdam Nilsson
- Keyword(s)
- Distributed Transcoding, Edge Computing, RIST, SRT, ARQ, Kubernetes, GPU Acceleration
- Copyright
- © 2026 SMPTE
Bibliographic Reference(s)
- Video Services Forum, “Reliable Internet Stream Transport (RIST) Protocol, TR-06-2,” 2020. EXTERNAL
- Haivision, “SRT Protocol Technical Overview,” SRT Alliance, 2018. EXTERNAL
- P. Paakkonen, “Online Architecture for Predicting Live Video Transcoding Resources,” J. of Cloud Comput., 8(10), 2019. EXTERNAL
- Archana Venkatraman, “BBC takes iPlayer video production to AWS cloud to beat bottleneck and storage woes,” ComputerWeeklycom 2014. [Online]. Available. https://www.computerweekly.com/news/2240233217/BBC-takes-iPlayer-video-production-to-AWS-cloud-to-beat-bottleneck-and-storage-woes. EXTERNAL
- Akamai, “How To Lower Your Live Video Transcoding Costs,” Akamai Blog Post, 2024. [Online]. Available: https://www.akamai.com/blog/developers/how-to-low-er-your-live-video-transcoding-costs. EXTERNAL
- Bitmovin, “Bitmovin's Distributed Encoding Architecture,” Bitmovin Docs, 2025. EXTERNAL
- European Broadcasting Union (EBU), “The Dynamic Media Facility Reference Archi-tecture,” White Paper, 2024. EXTERNAL
- Docker, Inc., “Swarm mode key concepts,” 2026. [Online]. Available: https://docs.docker.com/engine/swarm/key-concepts. EXTERNAL
- HashiCorp, Nomad, 2026. [Online]. Available: https://developer.hashicorp.com/nomad. EXTERNAL
- NVIDIA, “NVIDIA GPU Operator,” 2026. [Online]. Available. https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/overview.html. EXTERNAL
- Cloud Native Computing Foundation (CNCF), “Cloud Native 2024.” [Online], Available: https://www.cncf.io/reports/cncf-annual-survey-2024/. 2025. EXTERNAL
- NVIDIA, “NVIDIA Holoscan for Media, 2026.” [Online]. Available: https://developer.nvidia.com/holoscan-for-media. EXTERNAL
- Nils Knieling, AWS g6.xlarge pricing, 2026. [Online]. Available: https://aws-pricing.com/g6.xlarge.html. EXTERNAL
Source Data (JSON)
Full registry record with provenance metadata. Open directly: /api/doc/10.5594-JMI.2026-YPFV5033.json
Reference Tree
Explore all references and references to this document, as a navigable tree.
Open Reference TreeReference this Doc
Plain text (ISO 690 compliant)
Preview:
Pierre Le Fevre and Adam Nilsson; Integrating a Stream Transformation Engine in the Distribution Pipeline for Next-Gen Streaming Efficiency, MIJ 2026, Volume 135, Number 2 (pp. 42 to 49); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/YPFV5033
Snippet:
Pierre Le Fevre and Adam Nilsson; Integrating a Stream Transformation Engine in the Distribution Pipeline for Next-Gen Streaming Efficiency, MIJ 2026, Volume 135, Number 2 (pp. 42 to 49); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/YPFV5033
HTML (ISO 690 compliant)
Preview:
Pierre Le Fevre and Adam Nilsson; Integrating a Stream Transformation Engine in the Distribution Pipeline for Next-Gen Streaming Efficiency, MIJ 2026, Volume 135, Number 2 (pp. 42 to 49); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/YPFV5033
Snippet:
<span class="citation">Pierre Le Fevre and Adam Nilsson; <cite>Integrating a Stream Transformation Engine in the Distribution Pipeline for Next-Gen Streaming Efficiency</cite>, MIJ 2026, Volume 135, Number 2 (pp. 42 to 49); SMPTE, 2026. Available at <a href="https://doi.org/10.5594/JMI.2026/YPFV5033" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2026/YPFV5033</a></span>
SMPTE's HTML Pub
Preview:
Pierre Le Fevre and Adam Nilsson; Integrating a Stream Transformation Engine in the Distribution Pipeline for Next-Gen Streaming Efficiency, MIJ 2026, Volume 135, Number 2 (pp. 42 to 49); SMPTE, 2026
doi: 10.5594/JMI.2026/YPFV5033
url: https://doi.org/10.5594/JMI.2026/YPFV5033
doi: 10.5594/JMI.2026/YPFV5033
url: https://doi.org/10.5594/JMI.2026/YPFV5033
Snippet:
<li> Pierre Le Fevre and Adam Nilsson; <cite id="bib-10-5594-jmi-2026-ypfv5033">Integrating a Stream Transformation Engine in the Distribution Pipeline for Next-Gen Streaming Efficiency</cite>, MIJ 2026, Volume 135, Number 2 (pp. 42 to 49); SMPTE, 2026 <span class="doi">10.5594/JMI.2026/YPFV5033</span> </li>