TL;DR: In this blog, I share the hard-earned lessons I learned while designing a Zoom-like video conferencing system for a system design interview. You’ll learn how to define clear requirements, choose the right streaming protocols (WebRTC vs. RTMP vs. HLS), and architect scalable solutions using SFU over MCU models. I also cover how to handle network challenges (STUN/TURN, adaptive bitrate streaming), design a reliable signaling server, and integrate monitoring, analytics, and extensibility for features like recording and screen sharing. Whether you’re preparing for a Zoom system design interview platform or building real-time video apps, these 7 lessons will help you communicate tradeoffs clearly and design with scalability, reliability, and performance in mind.

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When I first faced a system design interview for a senior engineering role, the prompt was deceptively simple: Design a video conferencing system like Zoom. I thought, “How hard can it be?” Fast forward 3 hours of intense whiteboarding, network diagrams, and trade-off debates later — I realized designing a scalable, real-time, low-latency video platform is a beast. But through that struggle, I learned critical lessons that shaped my system design thinking.

In this post, I’ll walk you through my experience designing a Zoom-like system, breaking down my approach, technical tradeoffs, and real-world strategies. Whether you’re prepping for interviews or building collaboration tools, these 7 lessons will help you build robust, scalable video conferencing systems.

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1. Start with Clear Functional Requirements (Pro Tip: Define Scope First)

At the start, I made the rookie mistake of diving into architecture without pinning down what exactly the system needed to do.

For Zoom, this includes:

• Real-time video and audio streaming between multiple participants
• Support for groups of 2 to 100+ users
• Features like screen sharing, chat, recording, and meeting scheduling
• Low latency (under 200ms ideal)
• High availability and fault tolerance

I learned to start my system design by explicitly stating these requirements. It helps scope the system’s complexity and sets performance expectations upfront.

Lesson: Always nail down functional and non-functional requirements before sketching diagrams. Here’s a complete System Design guide you can refer to for more insight.

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2. Choose the Right Streaming Protocols: WebRTC vs. RTMP vs. HLS

One of the biggest technical decisions was about how to transmit video and audio data in real-time. For live video chat, latency and synchronization matter.

• WebRTC: Peer-to-peer protocol optimized for real-time, low-latency communication. Supports video, audio, and data channels.
• RTMP (Real-Time Messaging Protocol): Used for streaming to servers, not ideal for interactive calls.
• HLS (HTTP Live Streaming): Best for broadcast scenarios, but higher latency (~10s+), so no good for interactive calls.

I chose WebRTC because it’s the industry standard for real-time communications (Zoom uses this too). WebRTC also handles NAT traversal and firewall bypassing smoothly.

Takeaway: Use WebRTC for low-latency, interactive video conferencing. For one-to-many broadcasts, consider HLS or DASH.

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3. Architecting for Scalability: SFU vs. MCU (Real-World Example)

Next came the challenge of handling multiple participants. Should the server handle mixing streams, or clients?

• MCU (Multipoint Control Unit): Server mixes all media streams into one and sends a composite stream to clients. Simplifies client processing but is CPU-intensive and less scalable.
• SFU (Selective Forwarding Unit): Server routes individual streams to clients selectively without mixing—the clients handle compositing.

Zoom for large meetings uses SFU architecture, which scales better because the server forwards streams efficiently without heavy processing.

I sketched an SFU design:

• Clients send media streams to SFU
• SFU forwards streams to other participants based on presence and active speakers
• Enables bandwidth optimization, e.g., sending lower resolution streams to clients on poor networks

Lesson: Prefer SFU for scalable multi-party video conferencing. MCUs may suit small group calls or legacy systems.

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4. Tackling Network Challenges: NAT, Firewalls, and QoS

Dealing with real-world networks is brutal. During my mock interview, I was grilled on how to establish connections behind NATs and firewalls.

The answer: STUN/TURN servers.

• STUN (Session Traversal Utilities for NAT) helps clients discover their public IP and port.
• TURN (Traversal Using Relays around NAT) relays traffic when direct P2P connection fails.

Additionally, I learned:

• Implementing adaptive bitrate streaming and codec negotiation (VP8/VP9, H.264)
• Using RTP/RTCP for packet transmission and quality feedback
• Prioritizing packets for audio over video to maintain call quality

Framework: Build a robust signaling mechanism with STUN/TURN to handle diverse network situations gracefully.

Here’s an excellent explainer on ICE, STUN and TURN protocols by Mozilla: WebRTC Network Traversal.

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5. Designing the Signaling Server: The Unsung Hero

Many underestimate the signaling server’s role. It’s the glue that lets clients discover each other, exchange metadata (SDP), and establish connections.

I built a lightweight signaling layer using WebSockets for:

• User authentication and meeting room management
• Exchanging session descriptions (SDP) and ICE candidates
• Handling presence and meeting events

The signaling server was stateless with Redis for pub/sub to scale horizontally.

Actionable insight: Treat signaling separately; keep it low-latency, reliable, and horizontally scalable.

If you want a step-by-step guide, this bytebytego post on signaling is excellent.

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6. Monitoring, Analytics, and Debugging — Why They Matter

Zoom doesn’t just work… it works reliably at scale. One insight from my panel interview: how to detect and fix issues before users do.

Designing robust monitoring included:

• Real-time metrics for latency, packet loss, jitter per participant
• Logging connection state transitions and errors
• Heatmaps and call quality dashboards
• Alerting on unusual patterns (server load, dropped packets)

I realized this wasn’t just an afterthought — it’s baked into the architecture with hooks into the SFU and signaling layer.

Pro tip: Use tools like Prometheus+Grafana and custom telemetry to get granular visibility.

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7. Building for Extensibility: Recording, Screen Sharing, and Beyond

Lastly, interviewers asked about future features. Zoom isn’t just video—it’s a platform.

My extension framework included:

• Recording: Capture media streams at SFU or client side, store in blob storage (S3)
• Screen sharing: Treated as a special media stream with priority routing
• Chat and reactions: Leverage existing signaling and messaging backend

Planning extensibility upfront is key for maintainability as you add new features without re-architecting.

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Final Thoughts: From Interview to Real-World Impact

Designing a Zoom-like system pushed me to think deeply about real-time constraints, network complexity, and user experience. The lesson? Balancing scalability with maintainability requires tradeoffs and iterative refinement.

If you’re prepping for interviews, practice by designing systems you actually use daily — it anchors abstract patterns in reality.

Remember, every failure is a step to mastery. I bombed my first mock interview but learned from it, and you will too.

You’re closer than you think.

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Additional Reading:

• Educative’s System Design Interview Course
• DesignGurus.io System Design Tutorials