Integrating a Stream Transformation Engine in the Distribution Pipeline for Next-Gen Streaming Efficiency

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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 Fevre
Adam Nilsson
Keyword(s): Distributed Transcoding, Edge Computing, RIST, SRT, ARQ, Kubernetes, GPU Acceleration
Copyright: © 2026 SMPTE

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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

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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

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<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>

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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

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<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>