Shipping Media Over QUIC at Meta Scale for Instagram Live Ingest

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By Konstantin Tsoy, Aman Sharma, Hari Anil Kumar, Alan Frindell, Sandarsh Srivastava Meta has begun deploying Media over QUIC (MoQ), an emerging open protocol for real-time video streaming at scale. MoQ will help simplify our protocol stack for live streaming video and enhance overall user experience for live video. We’re sharing how we shipped MoQ on Instagram Live ingest, and what the future looks like from here. This work was also presented at DEMOQED 2026.

Konstantin Tsoy、Aman Sharma、Hari Anil Kumar、Alan Frindell 和 Sandarsh Srivastava 撰写。Meta 已开始部署 Media over QUIC (MoQ)，这是一种新兴的开放协议，旨在实现大规模实时视频流传输。MoQ 将有助于简化我们的直播视频协议栈，并提升直播视频的整体用户体验。我们在此分享如何在 Instagram Live 的采集端部署 MoQ，以及未来的发展方向。这项工作也已在 DEMOQED 2026 上发表。

Instagram Live now uses the open standard transport protocol Media over QUIC (MoQ) for live video ingest on iOS and Android. As a transport designed for both ingest and playback, MoQ gives us the foundation to build toward an architecture that is lower-latency, more resilient on unreliable networks, and more responsive for creators and viewers. It also gives teams across Meta a common foundation for bringing those improvements to future live and interactive experiences without rebuilding them on separate protocol stacks.

Instagram Live 现在在 iOS 和 Android 平台上使用开放标准传输协议 Media over QUIC (MoQ) 进行直播视频采集。作为一种专为采集和播放设计的传输协议，MoQ 为我们构建低延迟、在不可靠网络下更具弹性、且对创作者和观众更具响应性的架构奠定了基础。它还为 Meta 各个团队提供了一个共同的基础，以便将这些改进引入未来的直播和互动体验中，而无需在独立的协议栈上重复构建。

MoQ: A Media Protocol for Every Moment in Time The internet has accumulated a rich set of media protocols, each shaped by the problem in front of it. RTMP for ingestion. HLS and DASH for playback. WebRTC for real-time calls. RUSH for the cases that did not fit neatly into any of those. Each was a strong answer for its moment, and together they’ve carried the industry a long way.

MoQ：适用于每一时刻的媒体协议。互联网积累了丰富的媒体协议，每一种协议都是为了解决当时面临的问题而形成的。用于采集的 RTMP，用于播放的 HLS 和 DASH，用于实时通话的 WebRTC，以及用于无法完全适配上述协议场景的 RUSH。每一种协议在当时都是强有力的解决方案，它们共同推动了行业的发展。

At Meta, someone who starts a live stream and then makes a voice call on the same app touches three different protocol stacks, each with its own serving infrastructure, monitoring, and operational expertise. That separation made sense when these experiences were distinct. But as live video, real-time AI, and generative AI creation tools increasingly share the same product surface, the lines between these use cases are starting to blur. A common protocol across ingest, delivery, and playback can reduce the number of custom transports and operating models that engineering teams need to maintain and improve over time.

在 Meta，如果有人在同一个应用中开启直播，随后又进行语音通话，就会触及三个不同的协议栈，每个协议栈都有各自的服务基础设施、监控和运营专业知识。当这些体验截然不同时，这种分离是有意义的。但随着直播视频、实时 AI 和生成式 AI 创作工具越来越多地共享同一个产品界面，这些用例之间的界限开始变得模糊。在采集、分发和播放中使用统一的协议，可以减少工程团队长期需要维护和改进的自定义传输协议及运营模式的数量。

How MoQ Works MoQ is an emerging IETF standard that enables low-latency, scalable media delivery (live streaming, real-time communication) over the QUIC transport layer. It’s being developed collaboratively at the IETF, with active participation from Meta, Google, Akamai, Cisco, Cloudflare, and academia. For nearly a decade, Meta’s live streaming video has run on Reliable Ultra-low-latency Streaming over HTTP (RUSH), an in-house QUIC-based protocol built to move past RTMP. RUSH proved that QUIC could carry live media at scale and was a predecessor to MoQ. The lessons learned from running RUSH in production are part of what shaped our bet on MoQ.

MoQ 的工作原理。MoQ 是一项新兴的 IETF 标准，旨在通过 QUIC 传输层实现低延迟、可扩展的媒体分发（直播、实时通信）。它正在 IETF 协作开发，Meta、Google、Akamai、Cisco、Cloudflare 和学术界均积极参与其中。近十年来，Meta 的直播视频一直运行在 Reliable Ultra-low-latency Streaming over HTTP (RUSH) 上，这是一种基于 QUIC 的内部协议，旨在取代 RTMP。RUSH 证明了 QUIC 可以大规模承载直播媒体，它是 MoQ 的前身。从 RUSH 的生产运行中汲取的经验，是我们决定押注 MoQ 的部分原因。

The core abstraction in MoQ is a Track, an addressable stream of media objects that publishers push and subscribers receive through a network of relays. Relays cache and forward objects without parsing the media payload – they only need to understand the MoQ framing and delivery metadata. A few properties matter for using MoQ as a single transport across the full latency spectrum:

MoQ 的核心抽象是“轨道”（Track），这是一种可寻址的媒体对象流，发布者推送这些对象，订阅者通过中继网络接收。中继器在缓存和转发对象时无需解析媒体负载——它们只需要理解 MoQ 的帧结构和分发元数据。将 MoQ 用作覆盖全延迟范围的单一传输协议时，有几个特性至关重要：

Built on QUIC: QUIC lets independent streams make progress without TCP-style cross-stream head-of-line blocking. It also supports faster connection setup and connection migration. Meta’s earlier QUIC rollout achieved lower request errors, tail latency, and video stalls than HTTP/2. MoQ applies the same transport foundation to live and real-time media. MoQ would also automatically benefit from any feature added to QUIC (e.g., FEC).

基于 QUIC 构建：QUIC 允许独立的流在没有 TCP 式跨流队头阻塞的情况下进行传输。它还支持更快的连接建立和连接迁移。Meta 此前推出的 QUIC 相比 HTTP/2 实现了更低的请求错误率、尾延迟和视频卡顿。MoQ 将同样的传输基础应用于直播和实时媒体。MoQ 还将自动受益于添加到 QUIC 中的任何功能（例如 FEC）。

Native Fan-out and caching: MoQ relays are built-in caching and request/stream coalescing endpoints, so media streams scale through CDN relays out of the box. Request coalescing, caching, and fan-out are not new (live streaming already does them), but MoQ makes them part of the protocol design rather than a bolt-on, and it is designed to scale via relays more efficiently than traditional HTTP streaming.

原生分发与缓存：MoQ 中继器是内置的缓存和请求/流合并端点，因此媒体流可以通过 CDN 中继器实现开箱即用的扩展。请求合并、缓存和分发并非新鲜事（直播中已有应用），但 MoQ 将其作为协议设计的一部分，而非附加功能，并且其设计旨在比传统的 HTTP 流媒体更高效地通过中继器进行扩展。

Tunable Latency per use case: Two subscribers to the same track can run at different latencies at the same time — e.g., ~100 ms for an interactive participant and several seconds for an archival or buffered service — based on tunable per-subscription delivery properties.

针对不同用例的可调延迟：同一轨道的两个订阅者可以同时以不同的延迟运行——例如，互动参与者约为 100 毫秒，而存档或缓冲服务则为几秒钟——这基于可调的每订阅分发属性。

Track-aware delivery: Relays can drop, prioritize, or replay objects based on metadata, giving better congestion behavior under loss than byte-stream protocols. First-class media context: Tracks and their quality variants are modeled in the protocol itself, so adaptive bitrate logic is built directly on native MoQ constructs rather than reconstructed at higher layers.

轨道感知分发：中继器可以根据元数据丢弃、优先处理或重放对象，在丢包情况下比字节流协议提供更好的拥塞行为。一流的媒体上下文：轨道及其质量变体在协议本身中建模，因此自适应比特率逻辑直接构建在原生的 MoQ 结构上，而不是在更高层进行重构。

Standards-based TCP fallback: When QUIC is blocked, MoQ can fall back to TCP through a standardized mapping (e.g., WebTransport over HTTP/2, or the emerging QMUX mapping), so a blocked network doesn’t force the ecosystem back into multiple parallel transports. MoQ delivers low latency at high scale, while also satisfying the full spectrum of use cases.

基于标准的 TCP 回退：当 QUIC 被阻塞时，MoQ 可以通过标准化的映射（例如 WebTransport over HTTP/2 或新兴的 QMUX 映射）回退到 TCP，因此被阻塞的网络不会迫使生态系统回到多个并行传输的状态。MoQ 在大规模环境下提供低延迟，同时满足全方位的用例需求。

First Deployment at Scale: Instagram Live Of all the surfaces MoQ can serve at Meta, we chose to ship live video ingest first for three reasons: Instagram Live is our largest-scale test: Instagram Live serves users at Meta scale. Proving MoQ at this scale clears the bar for the most demanding ingest workloads, and is complementary to the low-latency properties that matter for real-time use cases.

首次大规模部署：Instagram Live。在 Meta 所有可以使用 MoQ 的场景中，我们选择首先在直播视频采集端进行部署，原因有三：Instagram Live 是我们最大规模的测试：Instagram Live 服务于 Meta 规模的用户。在此规模下验证 MoQ，为最严苛的采集工作负载扫清了障碍，并与实时用例所需的低延迟特性相辅相成。

RUSH was falling behind the open ecosystem: Live ingest had been running on RUSH, Meta’s pragmatic QUIC-based answer to RTMP. RUSH was stable and performant at scale for nearly a decade, but the move to MoQ shifts development from a protocol operated primarily by Meta to a shared standards effort. Multiple implementers can contribute experience, test interoperability, and improve the specification instead of Meta evolving the transport largely on its own.

RUSH 已落后于开放生态系统：直播采集一直运行在 RUSH 上，这是 Meta 对 RTMP 的务实 QUIC 解决方案。RUSH 在近十年里在大规模应用中保持了稳定和高性能，但转向 MoQ 将开发工作从主要由 Meta 运营的协议转变为共享的标准工作。多个实现者可以贡献经验、测试互操作性并改进规范，而不是由 Meta 独自演进该传输协议。

Metric parity is a prerequisite for everything else: Proving MoQ can operate at Meta scale for its largest live surface without regression gives us a clean baseline from which to explore, including live playback among others. Rolling out MoQ on Instagram Live Repla

指标对等是其他一切的前提：证明 MoQ 可以在 Meta 最大的直播场景中大规模运行且不出现性能倒退，这为我们后续的探索提供了一个清晰的基准，包括直播播放等其他功能。在 Instagram Live 上推出 MoQ Repla