Architecture
Scaling
Infrastructure
Engineering
Scaling Streaming Architecture to 100M+ Concurrent Viewers
The engineering principles behind ultra-scale live streaming infrastructure
MR
Marcus Rivera
VP of Engineering, WAVE
Streaming to 100 million concurrent viewers is not just about having more servers. It requires a fundamental rethinking of architecture, from edge-first design to predictive scaling, from multi-CDN orchestration to graceful degradation patterns.
In this deep dive, we will explore the architectural patterns, engineering challenges, and practical solutions that enable streaming at truly massive scale. Whether you are preparing for your first viral moment or engineering for consistent high-scale events, these principles will guide your infrastructure decisions.
What Does 100M Scale Look Like?
Before diving into architecture, let us establish the scale we are discussing. These are real numbers from production streaming events.
127M
Peak Concurrent Viewers
World Cup Final 2024
180 Tbps
Peak Bandwidth
Sustained during major events
300+
Edge PoPs
Global deployment
23ms
Median Latency
Edge to viewer globally
<100ms
Origin-to-Edge
99th percentile
<500ms
Failover Time
Automatic CDN switching
The Five Core Challenges of Scale
Every streaming platform faces these fundamental challenges as they scale. Understanding them is the first step to solving them.
Challenge 1
Thundering Herd
Millions of viewers connecting simultaneously when a live event starts
Solution
Progressive rollout with queue-based admission, edge connection pooling, and predictive pre-warming
Impact
Prevents origin overload during flash traffic spikes
Challenge 2
Geographic Distribution
Viewers spread across 190+ countries with varying network conditions
Solution
Multi-tier edge network with 300+ PoPs, regional failover, and intelligent routing
Impact
Sub-50ms latency to 95% of global population
Challenge 3
Protocol Diversity
Supporting WebRTC, HLS, DASH, SRT, and legacy RTMP simultaneously
Solution
Protocol-agnostic core with edge transcoding and format negotiation
Impact
Single origin stream serves all client types efficiently
Challenge 4
State Management
Real-time chat, reactions, and viewer presence at massive scale
Solution
Distributed CRDT-based state with eventual consistency and local-first architecture
Impact
Millions of concurrent interactions without bottlenecks
Challenge 5
Failure Cascades
Single component failure affecting millions of viewers
Solution
Circuit breakers, bulkheads, and graceful degradation at every layer
Impact
99.99% uptime even during partial outages
The Four-Tier Architecture
A scalable streaming platform is built in layers, each with distinct responsibilities and scaling characteristics.
Origin Layer
Ingest, transcoding, and primary storage
Components
- Multi-region origin clusters (3+ regions)
- GPU-accelerated transcoding farms
- Object storage for VOD and DVR
- Real-time ABR ladder generation
- Watermarking and DRM injection
Scaling Strategy
Horizontal pod autoscaling based on ingest load
Capacity
10,000 concurrent live streams per region
Distribution Layer
CDN orchestration and protocol conversion
Components
- Multi-CDN load balancing (4+ providers)
- Edge transcoding for protocol conversion
- Manifest manipulation for personalization
- Token authentication at edge
- Real-time traffic shaping
Scaling Strategy
Traffic-based CDN weight shifting
Capacity
50M+ concurrent streams globally
Edge Layer
Last-mile delivery and client interaction
Components
- 300+ global edge PoPs
- WebRTC SFUs for ultra-low latency
- HLS/DASH segment caching
- Connection pooling and multiplexing
- Client telemetry collection
Scaling Strategy
Geographic auto-scaling with predictive provisioning
Capacity
100M+ concurrent connections
Real-time Layer
Chat, reactions, and presence
Components
- Distributed WebSocket clusters
- CRDT-based state synchronization
- Pub/sub message routing
- Presence aggregation services
- Moderation pipeline integration
Scaling Strategy
Sharded by stream ID with dynamic rebalancing
Capacity
10M+ messages per second
Essential Scaling Patterns
These architectural patterns are battle-tested across thousands of high-scale streaming events.
Pattern 1
Edge-First Architecture
Push computation and caching to the edge, minimize origin traffic
Implementation
- Cache manifest and segments at edge with TTLs
- Perform token validation at edge (no origin roundtrip)
- Edge-side includes for personalization
- Protocol transcoding at edge PoPs
Key Benefit
Reduces origin load by 99%, improves latency by 10-50x
Pattern 2
Multi-CDN with Active Failover
Distribute traffic across multiple CDN providers with real-time health monitoring
Implementation
- Weighted round-robin across 4+ CDN providers
- Real-time error rate monitoring per CDN
- Automatic traffic shifting on degradation
- Geographic preference optimization
Key Benefit
Zero-downtime during CDN outages, cost optimization
Pattern 3
Predictive Pre-warming
Anticipate traffic spikes and pre-scale infrastructure
Implementation
- ML-based traffic prediction from historical patterns
- Event calendar integration for scheduled streams
- Social media sentiment analysis for viral detection
- Proactive edge node provisioning
Key Benefit
Handles 10x traffic spikes without degradation
Pattern 4
Graceful Degradation
Maintain core functionality when subsystems fail
Implementation
- Circuit breakers on all external dependencies
- Fallback to cached content when origin is unavailable
- Chat degradation to polling when WebSocket fails
- Quality downgrade rather than stream interruption
Key Benefit
Users experience reduced quality, never complete failure
Scaling Milestones: A Roadmap
Architecture evolves with scale. Here is what changes at each order of magnitude.
| Viewers | Architecture | Key Challenges | WAVE Tier |
|---|---|---|---|
| 10K | Single Origin + CDN | Basic caching, simple failover | Starter tier supports this out of the box |
| 100K | Multi-region Origin + Multi-CDN | Geographic distribution, protocol diversity | Professional tier with 2 regions, 2 CDNs |
| 1M | Distributed Origin + Edge Compute | Real-time features at scale, state management | Business tier with edge transcoding |
| 10M | Full Edge-First + Predictive Scaling | Thundering herd, cascading failures | Enterprise tier with dedicated capacity |
| 100M+ | Global Mesh + Custom Infrastructure | ISP-level partnerships, custom edge nodes | Elite tier with guaranteed SLAs |
Common Pitfalls to Avoid
Learn from the mistakes of others. These are the most common scaling pitfalls we have seen in production.
Ignoring cache invalidation complexity
Consequence
Stale content served, manifest sync issues
Solution
Use versioned URLs, implement proper TTLs, plan for emergency purges
Single-CDN dependency
Consequence
Complete outage when CDN has issues
Solution
Multi-CDN from day one, even at small scale
Underestimating state management
Consequence
Chat and reactions become bottleneck
Solution
Design stateless where possible, use distributed state stores
No graceful degradation plan
Consequence
Complete failure instead of reduced experience
Solution
Design fallbacks for every critical path
Testing only happy paths
Consequence
Unknown behavior under failure conditions
Solution
Chaos engineering, game days, failure injection
Essential Monitoring Metrics
You cannot scale what you cannot measure. These are the metrics that matter at scale.
Delivery
- Rebuffer rate
- Startup time
- Error rate
- Bitrate distribution
Infrastructure
- Edge cache hit ratio
- Origin load
- CDN health
- Network saturation
Experience
- Video start time
- Time to first byte
- Quality changes
- Session duration
Business
- Concurrent viewers
- Peak capacity
- Cost per viewer
- Revenue per stream
Case Study: WAVE Platform: 100M Viewer Architecture
The Challenge
Support a major global sports event with 100M+ peak concurrent viewers, sub-500ms latency for betting integration, and zero tolerance for downtime.
Architecture Decisions
- Deployed to 12 origin regions with active-active failover
- Engaged 6 CDN providers with real-time traffic steering
- Pre-positioned edge compute in 50 major metros
- Implemented WebRTC for betting-critical low-latency feed
- Built custom admission control to handle connection surge
Results
127M
Peak viewers
340ms
Avg latency
99.998%
Uptime
major incidents
Zero
Key Learnings
- Pre-warming is essential - start 2 hours before event
- Multi-CDN saved us during a provider outage mid-event
- Real-time dashboards enabled rapid incident response
- Rehearsals with simulated load revealed critical bugs
Key Takeaways
Think in Layers
Each tier has different scaling characteristics. Design accordingly.
Edge-First Always
Push computation to the edge. Your origin should be a last resort.
Plan for Failure
At scale, something will always fail. Design for graceful degradation.
Measure Everything
You cannot scale what you cannot observe. Invest in monitoring early.
Multi-Everything
Multi-region, multi-CDN, multi-protocol. Redundancy is not optional.
Pre-warm Aggressively
Predictive scaling beats reactive scaling every time.
MR
Marcus Rivera
VP of Engineering, WAVE
Marcus has led streaming infrastructure teams at three unicorn startups and has architected systems serving over 500 million users. He focuses on building resilient, scalable video delivery systems.