Scaling Telemedicine Platforms: Architecture for 10,000+ Concurrent Video Sessions

The telemedicine market has grown from a niche offering to a core healthcare delivery channel. Platforms that started handling dozens of daily consultations now need to support thousands of concurrent video sessions with clinical-grade reliability. Here's how we approach the architecture.

The Challenge: Real-Time Video at Scale

Video consultations are the most demanding feature in a telemedicine platform. Unlike pre-recorded video streaming (Netflix-style), telehealth requires:

• Ultra-low latency: Sub-200ms round-trip for natural conversation
• Bidirectional streams: Both doctor and patient transmit simultaneously
• Adaptive quality: Graceful degradation on poor networks (common in rural healthcare)
• HIPAA compliance: End-to-end encryption, no data stored on intermediate servers
• Clinical reliability: 99.95%+ uptime — a dropped call during a consultation is unacceptable

WebRTC: The Foundation

WebRTC is the only viable technology for browser-based real-time video. It provides peer-to-peer encrypted media streams with adaptive bitrate. But raw WebRTC doesn't scale — you need infrastructure around it.

The key components of a WebRTC infrastructure:

• Signaling Server: Exchanges session metadata (SDP offers/answers, ICE candidates) between peers. We use WebSocket connections through a load-balanced Node.js service.
• STUN Servers: Help peers discover their public IP addresses for direct connections. We deploy STUN servers in multiple regions.
• TURN Servers: Relay media when direct peer-to-peer connections fail (approximately 15-20% of sessions). These are the most resource-intensive components.
• SFU (Selective Forwarding Unit): For group consultations (e.g., specialist consults with multiple providers), an SFU selectively routes video streams more efficiently than mesh topology.

Architecture Overview

Our production telemedicine architecture follows a multi-region, active-active design:

┌──────────────────────────────────────────────────────┐
│                   Global Load Balancer               │
│              (Geolocation-based routing)              │
└───────────┬──────────────────────┬───────────────────┘
            │                      │
    ┌───────▼───────┐      ┌──────▼────────┐
    │  US-East       │      │  US-West      │
    │  Region        │      │  Region       │
    │                │      │               │
    │  ┌──────────┐  │      │  ┌──────────┐ │
    │  │Signal SVC│  │      │  │Signal SVC│ │
    │  └──────────┘  │      │  └──────────┘ │
    │  ┌──────────┐  │      │  ┌──────────┐ │
    │  │TURN Relay│  │      │  │TURN Relay│ │
    │  └──────────┘  │      │  └──────────┘ │
    │  ┌──────────┐  │      │  ┌──────────┐ │
    │  │SFU Nodes │  │      │  │SFU Nodes │ │
    │  └──────────┘  │      │  └──────────┘ │
    │  ┌──────────┐  │      │  ┌──────────┐ │
    │  │  API SVC  │  │      │  │  API SVC │ │
    │  └──────────┘  │      │  └──────────┘ │
    └───────────────┘      └───────────────┘

TURN Server Scaling

TURN servers consume the most bandwidth and compute. Each relayed session requires the TURN server to receive and retransmit both audio and video streams. For planning purposes:

• A standard 720p video consultation uses approximately 1.5 Mbps per direction
• A TURN server relays both directions, so each session uses approximately 3 Mbps of TURN bandwidth
• A single TURN server with 1 Gbps networking can handle approximately 300 concurrent relayed sessions
• At 20% relay rate, supporting 10,000 concurrent sessions requires approximately 7 TURN servers with headroom

We deploy TURN servers on dedicated instances with premium network performance and auto-scale based on active relay connections.

Network Resilience for Clinical Grade Reliability

Dropped video calls during medical consultations are not acceptable. We implement multiple layers of resilience:

• ICE restart: Automatically renegotiate the connection when network conditions change (e.g., WiFi to cellular handoff)
• Adaptive bitrate: Dynamically adjust video quality based on available bandwidth. Drop to audio-only if necessary rather than disconnecting
• Session persistence: If a connection drops, automatically reconnect to the same session within a 60-second window without requiring the user to rejoin
• Multi-path redundancy: Attempt direct connection, STUN-assisted connection, and TURN relay simultaneously, using the best available path

Monitoring and Observability

We instrument every video session with real-time metrics:

• Round-trip time (RTT) between peers
• Packet loss percentage
• Video resolution and frame rate
• Audio jitter buffer size
• Connection state transitions

These metrics feed into a real-time dashboard that our operations team monitors during peak hours, with automated alerts for degraded quality patterns that might indicate infrastructure issues.

Key Takeaways

Building telemedicine at scale requires specialized infrastructure that goes far beyond a simple WebRTC implementation. The investment in TURN server capacity, multi-region deployment, adaptive quality algorithms, and comprehensive monitoring is what separates a demo from a clinical-grade platform that healthcare providers trust for patient care.