Ad Insertion for MOQ (Media over QUIC): What Is Possible Today and What Comes Next
To continue my coverage of all things MOQ , today I want to touch on something that keeps coming up in MOQ discussions: ad insertion. Ultra-low latency delivery is only part of the equation. For MOQ to be viable in real production environments, monetization has to work too. That means clean ad signaling, measurable delivery, and architectures that scale without breaking synchronization. IETF MOQ interim at Google’s Boulder campus This was one of the topics at the recent IETF MOQ interim at Google’s Boulder campus in February 2026. Watch the video below, where I discuss this event with my teammate Paul Gregoire , Red5 Solutions Architect, who attended it. To better understand how these concepts apply in practice, here are the key definitions used in ad insertion workflows for MOQ: Server-Si
To continue my coverage of all things MOQ, today I want to touch on something that keeps coming up in MOQ discussions: ad insertion.
Ultra-low latency delivery is only part of the equation. For MOQ to be viable in real production environments, monetization has to work too. That means clean ad signaling, measurable delivery, and architectures that scale without breaking synchronization.
IETF MOQ interim at Google’s Boulder campus
This was one of the topics at the recent IETF MOQ interim at Google’s Boulder campus in February 2026. Watch the video below, where I discuss this event with my teammate Paul Gregoire, Red5 Solutions Architect, who attended it.
To better understand how these concepts apply in practice, here are the key definitions used in ad insertion workflows for MOQ:
-
Server-Side Ad Insertion (SSAI): ads are stitched directly into the video stream on the server before the stream is delivered to viewers.
-
Server-Guided Ad Insertion (SGAI): the server signals ad opportunities and decides when and which ads to request and play.
-
Client-Side Ad Insertion (CSAI): the video player on the viewer’s device requests, loads, and inserts ads during playback instead of receiving a prestitched stream.
-
Regional blackout: a restriction that blocks or replaces live content for viewers in certain geographic areas due to licensing or local broadcast rights.
Gwendal Simon from Synamedia and Will Law from Akamai Technologies presented how MOQ and the MOQ Transport Streaming Format (MSF) can carry SCTE-35 signaling for Server-Guided Ad Insertion (SGAI) as well as other control scenarios such as regional blackout enforcement.
Figuring out how content works with advertising and blackout use cases in a MOQ environment is critical for the technology to support real-world media and entertainment applications. Because of that, the topic is receiving significant attention across the MOQ community. The problem is not fully solved yet, but progress is moving quickly as new approaches and demonstrations continue to emerge.
Gwendal and Will presented what is possible today: a working architecture where ad decisioning systems publish SCTE-35 signaling as structured events on a dedicated Event Timeline track, while media and advertising streams remain separate and independently distributed over MOQ. Subscribers can follow these signals in real-time to switch between media and ad tracks, fetch ad content dynamically using identifiers such as MOQ URLs, and return to the primary program stream at the correct playback moment.
Learn more from the files they presented at the event.
Ad Insertion in MOQ — Interim Meeting BoulderDownload
SGAI Over MOQ_ SCTE35-Based Event Timeline Type DefinitionDownload_
The approach demonstrates how existing broadcast signaling models can operate over MOQ without embedding cues directly inside the video stream, allowing signaling and media delivery to scale independently while preserving the timing relationships needed for live playback.
Does Red5 Support Ad Insertion for MOQ?
We are actively working on this area with partners like Showfer Media, integrating ad workflows into real-time streaming pipelines so MOQ can move from experimental to production-ready systems. If you are interested in learning more, visit us at the NAB Show 2026, where we will showcase MOQ and TrueTime Solutions™ in action at the Nomad Media booth W2357 and AWS booth W1701. Reach out to us here to schedule a meeting at NAB. Our schedule is filling up quickly, so it is best to plan ahead.
Become a Beta Tester of MOQ Streaming Powered by Red5 and CacheFly
Beyond the live demos, we will introduce how Red5 Cloud will deliver MOQ at scale through our partnership with CacheFly. In this workflow, live streams are ingested into Red5, processed through our video packaging layer, and delivered via CacheFly using either MOQ or HTTP-based protocols like HLS and LL-HLS. This gives customers flexibility. You can deliver ultra-low latency streams with MOQ where real-time performance matters most, while still supporting traditional formats for broader device compatibility. It is not about replacing one protocol with another. It is about choosing what works best for your use case.
Our teams will be collecting beta testers at NAB for our globally deployed MOQ network. It allows Red5 Cloud users to leverage CacheFly CDN for MOQ delivery. If you want in on this early or just want some more details on how it might work for your business, reach out to us using this link.
Conclusion
Ad insertion for MOQ is evolving quickly as the industry works to make real-time streaming monetizable without sacrificing synchronization or scale. While approaches like SCTE-35–based signaling and SGAI over MOQ already show strong potential, the ecosystem is still maturing as partners continue building production-ready workflows. With ongoing collaboration and real-world testing, MOQ is moving closer to supporting reliable, scalable ad-supported streaming. For a deeper look at how MOQ compares to existing transport approaches, read our “SRT vs MOQT” blog.
DEV Community
https://dev.to/red5/ad-insertion-for-moq-media-over-quic-what-is-possible-today-and-what-comes-next-102jSign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
modelproductapplication
The portability paradox of foundation models for clinical decision support
npj Digital Medicine, Published online: 07 April 2026; doi:10.1038/s41746-026-02615-4 Yakdan et al. demonstrate that foundation models (FMs) trained to predict cervical spondylotic myelopathy from electronic health record data outperform traditional models on internal datasets but lose their advantage during external validation. This suggests that the feature-dense patterns learned by FMs may reduce their portability across settings, particularly for rare outcomes. As FMs approach clinical deployment, local validation, subgroup analysis, and attention to implementation burden are essential to inform health system planning and stewardship.

Robust Regression with Adaptive Contamination in Response: Optimal Rates and Computational Barriers
arXiv:2604.04228v1 Announce Type: cross Abstract: We study robust regression under a contamination model in which covariates are clean while the responses may be corrupted in an adaptive manner. Unlike the classical Huber's contamination model, where both covariates and responses may be contaminated and consistent estimation is impossible when the contamination proportion is a non-vanishing constant, it turns out that the clean-covariate setting admits strictly improved statistical guarantees. Specifically, we show that the additional information in the clean covariates can be carefully exploited to construct an estimator that achieves a better estimation rate than that attainable under Huber contamination. In contrast to the Huber model, this improved rate implies consistency even when th

The Geometric Alignment Tax: Tokenization vs. Continuous Geometry in Scientific Foundation Models
arXiv:2604.04155v1 Announce Type: cross Abstract: Foundation models for biology and physics optimize predictive accuracy, but their internal representations systematically fail to preserve the continuous geometry of the systems they model. We identify the root cause: the Geometric Alignment Tax, an intrinsic cost of forcing continuous manifolds through discrete categorical bottlenecks. Controlled ablations on synthetic dynamical systems demonstrate that replacing cross-entropy with a continuous head on an identical encoder reduces geometric distortion by up to 8.5x, while learned codebooks exhibit a non-monotonic double bind where finer quantization worsens geometry despite improving reconstruction. Under continuous objectives, three architectures differ by 1.3x; under discrete tokenizatio
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products

Fused Multinomial Logistic Regression Utilizing Summary-Level External Machine-learning Information
arXiv:2604.03939v1 Announce Type: cross Abstract: In many modern applications, a carefully designed primary study provides individual-level data for interpretable modeling, while summary-level external information is available through black-box, efficient, and nonparametric machine-learning predictions. Although summary-level external information has been studied in the data integration literature, there is limited methodology for leveraging external nonparametric machine-learning predictions to improve statistical inference in the primary study. We propose a general empirical-likelihood framework that incorporates external predictions through moment constraints. An advantage of nonparametric machine-learning prediction is that it induces a rich class of valid moment restrictions that rema

Vision Transformer-Based Time-Series Image Reconstruction for Cloud-Filling Applications
arXiv:2506.19591v2 Announce Type: replace-cross Abstract: Cloud cover in multispectral imagery (MSI) poses significant challenges for early season crop mapping, as it leads to missing or corrupted spectral information. Synthetic aperture radar (SAR) data, which is not affected by cloud interference, offers a complementary solution, but lack sufficient spectral detail for precise crop mapping. To address this, we propose a novel framework, Time-series MSI Image Reconstruction using Vision Transformer (ViT), to reconstruct MSI data in cloud-covered regions by leveraging the temporal coherence of MSI and the complementary information from SAR from the attention mechanism. Comprehensive experiments, using rigorous reconstruction evaluation metrics, demonstrate that Time-series ViT framework si

Apparent Age Estimation: Challenges and Outcomes
arXiv:2604.03335v1 Announce Type: cross Abstract: Apparent age estimation is a valuable tool for business personalization, yet current models frequently exhibit demographic biases. We review prior works on the DEX method by applying distribution learning techniques such as Mean-Variance Loss (MVL) and Adaptive Mean-Residue Loss (AMRL), and evaluate them in both accuracy and fairness. Using IMDB-WIKI, APPA-REAL, and FairFace, we demonstrate that while AMRL achieves state-of-the-art accuracy, trade-offs between precision and demographic equity persist. Despite clear age clustering in UMAP embeddings, our saliency maps indicate inconsistent feature focus across demographics, leading to significant performance degradation for Asian and African American populations. We argue that technical impr

NAIMA: Semantics Aware RGB Guided Depth Super-Resolution
arXiv:2604.04407v1 Announce Type: new Abstract: Guided depth super-resolution (GDSR) is a multi-modal approach for depth map super-resolution that relies on a low-resolution depth map and a high-resolution RGB image to restore finer structural details. However, the misleading color and texture cues indicating depth discontinuities in RGB images often lead to artifacts and blurred depth boundaries in the generated depth map. We propose a solution that introduces global contextual semantic priors, generated from pretrained vision transformer token embeddings. Our approach to distilling semantic knowledge from pretrained token embeddings is motivated by their demonstrated effectiveness in related monocular depth estimation tasks. We introduce a Guided Token Attention (GTA) module, which itera


Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!