Estimate network bandwidth
for multi-camera AI systems
Input camera count, resolution, frame rate, encoding format, and uplink constraint. The engine calculates total bitrate, recommends switch spec, and flags bottlenecks.
// Define requirements
// Bandwidth Estimate
// Per-stream breakdown
// Infrastructure recommendations
What this Network Bandwidth Estimator decides
This tool estimates required network capacity for a multi-camera edge AI deployment based on five decision inputs: camera count, resolution, frame rate, video codec, and uplink constraint. It is designed for engineers planning camera networks, uplinks, and switch infrastructure for local recording, remote monitoring, and distributed edge deployments.
Camera count, resolution, and frame rate determine the base stream volume generated by the deployment.
The selected encoding format changes compression efficiency dramatically, which directly affects required bandwidth and storage rate.
Available uplink bandwidth determines whether full-fidelity remote transport is realistic or whether local recording, transcoding, or event-based forwarding is needed.
The estimator calculates per-stream bitrate from resolution, frame rate, and codec profile, scales that to total aggregate bitrate, and compares the result to the available uplink class. It then recommends the appropriate switch tier and flags conditions where local buffering, compression changes, or higher-bandwidth infrastructure become necessary.
- Per-stream bitrate estimate based on video profile
- Total aggregate bandwidth across all concurrent cameras
- Storage growth rate in GB per hour
- Uplink bottleneck detection for remote transport scenarios
- Recommended switch and network class based on aggregate load
- Per-stream bitrate: estimated Mbps for each camera stream
- Total aggregate bitrate: combined network demand across all cameras
- Storage rate: estimated data generation per hour for local recording
- Uplink fit: whether the available uplink can support full-fidelity transmission
- Infrastructure recommendations: switch class, edge transcoding need, or storage architecture guidance
- Machine-readable JSON: a structured result for copying, sharing, or downstream reuse
{
"schema": "edgeaistack/network-bandwidth/v1",
"inputs": {
"camera_count": 8,
"resolution": "1080p",
"fps": 30,
"codec": "h264_high",
"uplink": "100mbps"
},
"computed": {
"per_stream_mbps": 4.0,
"total_aggregate_mbps": 32.0,
"storage_gb_per_hour": 14.1,
"uplink_bottleneck": false,
"switch_recommendation": "Netgear GS308E (8-port smart GbE)"
}
}
Why does codec choice matter so much?
Codec efficiency changes how much bandwidth each stream consumes. H.265 and AV1 generally reduce required bandwidth relative to H.264, while MJPEG and raw video can multiply total network load significantly.
Does low uplink bandwidth make the deployment impossible?
No. It usually means the deployment should favor local recording, edge transcoding, lower-bitrate secondary streams, or event-triggered remote upload instead of always-on full-fidelity forwarding.
When is 10GbE usually required?
10GbE is typically required for denser multi-camera deployments, high-resolution workflows, or low-compression streams where total aggregate bitrate approaches or exceeds practical gigabit switching headroom.
Does this tool also estimate storage impact?
Yes. The estimator also computes approximate storage growth in GB per hour so you can plan NVR, NAS, or retention strategy alongside bandwidth capacity.