Your cameras got smart. Your command centre is still blind. Here’s the layer in between.

A modern truck already sees more than its driver does. The road-facing camera watches for tailgating and lane drift; the in-cab camera watches the driver for drowsiness and distraction; both flag events the instant they happen and save the clip — without sending a single frame anywhere. The hardware is genuinely intelligent. So here is the question that trips up nearly every operator building out their fleet video infrastructure: when a safety manager wants to watch truck 47 live, right now, as an incident unfolds — why can’t they?

The answer isn’t that the cameras are weak. It’s that live monitoring was never their job.

What Edge AI Actually Does Well (More Than You Think)

Before talking about what edge can’t do, it’s worth being precise about what it does — because it’s a lot, and any honest picture starts here. Modern MDVRs (mobile digital video recorders) and AI dashcams run inference directly on the device:

• ADAS (advanced driver assistance) — detecting tailgating, lane departure, and forward-collision risk from the road-facing camera.
• DMS (driver monitoring) — detecting drowsiness, phone use, distraction, and smoking from the cabin-facing camera.
• Event capture — when something crosses a threshold, the device saves the relevant clip locally.
• Event upload — that clip, plus metadata like GPS, speed, and G-force, is pushed to a back office when connectivity allows.

And it does all of this with three properties that are hard to beat: it works offline, because inference doesn’t need a network; it sips bandwidth, because you upload a 20-second event clip rather than a continuous stream; and the footage stays on the device until someone asks for it.

But notice what every one of those capabilities has in common. They happen after the fact, or in isolation — a clip, a flag, an upload. None of them puts a live human in the loop while something is still happening.

The Three Things Edge Physically Can’t Do

This is where the ceiling sits — and it isn’t a missing feature. It’s a structural limit of a camera bolted to a moving vehicle with a cellular link. Three capabilities stay permanently outside its reach.

1. Live multi-viewer streaming. An edge device can record continuously, but it cannot serve a live feed to several people at once. A dispatcher, a safety lead, and an insurer can’t all watch the same truck simultaneously in real time — the device has neither the uplink nor the fan-out logic to do it.

2. Real-time remote diagnostics and interaction. Reviewing an uploaded clip tomorrow is not the same as seeing a situation as it develops and responding to it. Pulling a live view on demand — “show me that cabin now” — is a different capability than “here’s what happened an hour ago.”

3. Command-centre monitoring at fleet scale. A control room watching a wall of live feeds — dozens or hundreds of vehicles, any one pullable to full screen the instant an alert fires — is not a bigger version of event upload. It’s a fundamentally different architecture.

These aren’t failures of edge AI. They’re simply a different job — one that lives in a different layer of the system.

Orthogonal, Not Superior: Why This Isn’t “Edge vs Cloud”

It’s tempting to read all this as “edge is limited, so move to the cloud.” That framing is wrong, and it leads to bad architecture.

Edge and the cloud layer aren’t competing for the same job. Edge AI belongs at the source: it’s the only place inference can happen instantly, offline, and without shipping video anywhere. The cloud — or on-prem — layer belongs in the middle: it’s the only place that can carry live media from a moving vehicle to human eyes and fan it out to many of them at once.

A real fleet needs both. Take away edge and you drown in bandwidth and lose offline detection. Take away the transport layer and your smart cameras can detect everything and show it to no one, live.

So if that middle layer’s job is to move live video from a moving vehicle to a command centre — what does it actually have to do to earn its place?

What Fleet Video Infrastructure Has to Get Right

The transport layer between the camera and the command centre — the fleet video infrastructure proper — has a specific and demanding job description. Five things it has to handle.

Survive the network a moving vehicle actually has. Cellular coverage on a highway drops, hands off between towers, and varies wildly. The layer has to hold a live session together across all of that, degrading gracefully instead of failing outright.

Fan one feed out to many viewers. One camera, several simultaneous watchers, without asking the vehicle to upload the same stream once per viewer. The multiplication happens in the infrastructure, not on the constrained uplink.

Keep latency low enough to call it “live.” A feed that is eight seconds behind reality is useless for an unfolding incident. Real-time monitoring needs sub-second delivery, not “eventually.”

Scale across the whole fleet. Ten vehicles is a demo. A thousand vehicles, each potentially streaming, with a control room watching any subset, is an infrastructure problem — concurrency, routing, and regional distribution all at once.

Sit on top of the cameras you already have. No fleet rips out its MDVRs to add live view. The layer has to work with the existing hardware and the protocols those cameras already speak.

Each of these is a hard problem on its own. Which is why the question of how a team gets this layer — rather than what it does — is worth understanding on its own terms.

How This Layer Gets Sourced: Three Approaches

There’s no single way to add live video infrastructure to a fleet platform. Broadly, teams take one of three paths, and each fits a different kind of company.

Open-source media servers. Projects like mediasoup, Janus, or LiveKit give you the core building blocks of a streaming layer for no license fee. They suit engineering-heavy teams that want full control and have the specialists to run a distributed media system in production. What you take on is the maintenance: scaling, monitoring, patching, and the on-call reality of keeping live infrastructure alive. Best when live video transport is close to your core product and you have the team to own it.

Cloud video APIs. Providers such as Agora, Daily, or Cloudflare’s real-time stack let you integrate live streaming through an SDK, running on their cloud, typically billed per participant-minute. They suit teams that need to ship fast and whose data can live on a third-party cloud. The trade-offs to weigh are where the video data resides and how per-minute cost behaves as a fleet grows and streams more often.

Commercial white-label platforms. Platforms in this category — Samvyo is one — provide the streaming layer as a deployable product you run on-premise, or as a managed cloud deployment on AWS, DigitalOcean, or Hetzner, usually under flat licensing and embeddable under your own brand. They suit telematics vendors that need live video inside their own product, want the media to stay within controlled infrastructure, and prefer a cost that doesn’t climb with every added stream. Where this approach doesn’t fit: if you have no live-monitoring requirement, a small vehicle count, or you’re only moving occasional event clips, a full infrastructure platform is more than the problem needs — the edge upload you already have is the right tool.

Which path fits depends entirely on what your platform is for. But whichever one a team chooses, the reason the choice exists at all comes back to the same distinction we started with.

The Bottom Line

Edge AI made cameras intelligent. It did not make them live. A dashcam can tell you a driver looked drowsy at 3:12 PM and hand you the clip — but it can’t put a safety manager in the passenger seat, watching, while the truck is still on the road. That gap isn’t a defect to be fixed at the edge. It’s a job for a different layer.

Understanding fleet video infrastructure starts with holding those two things apart: detection lives at the source, live transport lives in the middle, and a fleet that wants both real-time intelligence and real-time visibility needs each doing the job it’s actually built for.

What’s Next?

Want the engineering-level, architecture-first view of this layer? The companion technical deep-dive walks through the SFU that handles multi-viewer fan-out, how RTSP and on-vehicle camera feeds get bridged to a web browser, and the real latency budget from vehicle to command centre — piece by piece: link to the technical version.

Frequently Asked Questions

Can edge AI cameras stream live video?

In a limited way. Most AI dashcams and MDVRs are built to detect events on-device and upload short clips, not to sustain a continuous live feed to multiple viewers. A device may support an on-demand live pull, but serving many simultaneous watchers, at low latency, across a fleet is the job of a separate video transport layer — not the camera itself.

What is fleet video infrastructure?

It’s the layer that carries live and recorded video between vehicle cameras and the people who need to see it — handling streaming, multi-viewer fan-out, low-latency delivery, and scale across many vehicles. Edge devices handle on-vehicle detection and recording; the infrastructure layer handles moving that video, live, to a command centre or dashboard.

Why can’t a dashcam stream to multiple viewers at once?

Because multiplying a single feed to many viewers — “fan-out” — requires a server in the middle, and the vehicle’s cellular uplink can’t practically upload the same stream once per viewer. A camera can originate one stream; distributing it to many watchers is handled by infrastructure between the vehicle and the viewers.

Edge AI vs cloud video for fleets — what’s the difference?

Edge AI runs inference on the device — detecting driver and road events instantly, offline, without sending video anywhere. The cloud or on-prem video layer carries live media from the vehicle to human viewers and distributes it at scale. They’re complementary: edge is for detection at the source, the video layer is for live visibility. Neither replaces the other.

Do I need to replace my cameras to add live streaming?

Generally no. A well-designed video transport layer works on top of the MDVRs and cameras you already run, using the protocols those devices already speak. Replacing hardware is usually unnecessary to add live view — the missing piece is the layer between the camera and the viewer.

What latency is acceptable for live fleet monitoring?

For watching an incident as it unfolds, sub-second to low-single-digit-second latency is the practical target. Anything approaching the multi-second delays typical of broadcast-style streaming is too far behind reality to act on — which is why real-time monitoring and after-the-fact clip review are different technical problems.