Platform Profile: StreamVerse
For this capstone, we design the NSA for StreamVerse — a video/audio content platform serving 80M subscribers globally with original content, user-generated content, and live streaming.
| Attribute | Details |
|---|---|
| Subscribers | 80M globally across 45 countries |
| Content Library | 2M titles (video, audio, interactive) |
| Daily Streams | 500M stream starts per day |
| Creators | 200K active content creators + in-house studios |
| Content Types | Movies, series, live sports, UGC, podcasts, interactive stories |
| Current Pain | Cold-start problem, buffering in emerging markets, manual content tagging |
Scale Challenges
Content Platform Scale Problems:
- 500M daily stream starts — Each requires instant recommendation, auth, CDN routing
- 2M content titles — Metadata, encoding, rights management at massive scale
- 45 countries — Content rights vary by region; latency must be <100ms everywhere
- Real-time personalization — Homepage must be different for every user, every visit
- Creator tools — Upload, transcode, analytics, monetization — self-service at scale
Content Platform NSA Principles
StreamVerse Architectural Principles:
- Personalization-Native — Every surface personalized in real-time; no two users see the same experience
- Edge-First Delivery — Content and computation pushed to edge; users served from nearest node
- Content-as-Graph — All content is a node in a knowledge graph; relationships drive discovery
- Creator-Empowered — Self-service tools for upload, analytics, monetization; creator success = platform success
- Adaptive Quality — Every stream adapts to device, network, and user preference in real-time
- Data-Informed Everything — Every pixel on screen earned its place through experimentation
Target Architecture
StreamVerse North Star Architecture
flowchart TB
subgraph L5["🖥️ Client Experience Layer"]
direction LR
C1[Smart TV]
C2[Mobile]
C3[Web]
C4[Gaming Console]
end
subgraph L4["🎯 Personalization Engine"]
direction LR
P1[Recommendation API]
P2[Home Feed Builder]
P3[Search & Discovery]
P4[Notification Engine]
end
subgraph L3["🎬 Content Services"]
direction LR
S1[Catalog Service]
S2[Playback Service]
S3[Creator Platform]
S4[Rights Engine]
end
subgraph L2["📊 Data & ML Platform"]
direction LR
D1[Interaction Events]
D2[Content Graph]
D3[ML Models]
D4[A/B Platform]
end
subgraph L1["🌍 Global Infrastructure"]
direction LR
I1[Multi-Region Cloud]
I2[CDN / Edge]
I3[Encoding Farm]
I4[Observability]
end
L5 --> L4
L4 --> L3
L3 --> L2
L2 --> L1
style L5 fill:#e8f4f4,stroke:#3B9797
style L4 fill:#f0f4f8,stroke:#16476A
style L3 fill:#e8f4f4,stroke:#3B9797
style L2 fill:#f0f4f8,stroke:#16476A
style L1 fill:#e8f4f4,stroke:#3B9797
Content Pipeline
Content ingestion is the lifeblood of the platform — from upload to playable asset in under 2 hours for standard content:
Content Ingestion Pipeline
flowchart LR
U[Upload] --> V[Validation]
V --> T[Transcode
Multiple bitrates]
T --> AI[AI Processing
Tags, thumbnails, chapters]
AI --> QC[Quality Check]
QC --> R[Rights Check
Region availability]
R --> CDN[CDN Distribution
Edge pre-warm]
CDN --> Live[Available to Users]
style U fill:#e8f4f4,stroke:#3B9797
style AI fill:#f0f4f8,stroke:#16476A
style CDN fill:#3B9797,stroke:#3B9797,color:#fff
style Live fill:#132440,stroke:#132440,color:#fff
{
"content_pipeline": {
"ingestion": {
"upload_max_size": "200GB",
"supported_formats": ["MP4", "MKV", "MOV", "ProRes", "H.265"],
"validation": ["codec_check", "audio_levels", "resolution_verify"]
},
"transcoding": {
"profiles": [
{ "resolution": "4K HDR", "bitrate": "16 Mbps", "codec": "AV1" },
{ "resolution": "1080p", "bitrate": "5 Mbps", "codec": "H.265" },
{ "resolution": "720p", "bitrate": "2.5 Mbps", "codec": "H.264" },
{ "resolution": "480p", "bitrate": "1 Mbps", "codec": "H.264" }
],
"adaptive_streaming": "DASH + HLS"
},
"ai_enrichment": {
"auto_tags": "scene detection + object recognition",
"thumbnails": "AI-selected best frames per genre",
"chapters": "audio/scene boundary detection",
"subtitles": "whisper-based ASR + translation (20 languages)"
}
}
}Recommendation Engine
The recommendation system is StreamVerse's most critical competitive advantage — 80% of viewing hours come from recommendations:
| Stage | Purpose | Input | Output |
|---|---|---|---|
| Candidate Gen | Find 1000 candidates from 2M titles | User embeddings + content graph | ~1000 candidates |
| Ranking | Score each candidate for this user | User features + content features + context | Ranked list |
| Re-Ranking | Apply business rules (diversity, freshness) | Ranked list + business constraints | Final display order |
| Presentation | Optimize thumbnails + copy per user | User preferences + A/B assignment | Personalized UI |
Content Knowledge Graph:
Every piece of content is a node connected by multiple edge types:
- Genre / Mood / Theme — semantic relationships
- Cast / Creator — people-based connections
- Viewing Patterns — "users who watched X also watched Y"
- Series / Franchise — narrative continuity
- Temporal — trending now, seasonally popular, new release
Global Delivery Architecture
Delivering video to 80M users across 45 countries with sub-second start times requires a sophisticated multi-tier delivery system:
Global CDN Architecture
flowchart TD
subgraph Origin["Origin (3 regions)"]
O1[US-East Master]
O2[EU-West Master]
O3[APAC Master]
end
subgraph MidTier["Mid-Tier CDN"]
M1[US Regional]
M2[EU Regional]
M3[APAC Regional]
M4[LATAM Regional]
end
subgraph Edge["Edge (200+ PoPs)"]
E1[Edge Cache A]
E2[Edge Cache B]
E3[Edge Cache C]
E4[Edge Cache N]
end
subgraph Users["Users"]
U1[Smart TV]
U2[Mobile]
U3[Web]
end
Origin --> MidTier
MidTier --> Edge
Edge --> Users
style O1 fill:#16476A,stroke:#16476A,color:#fff
style M1 fill:#3B9797,stroke:#3B9797,color:#fff
style E1 fill:#e8f4f4,stroke:#3B9797
Real-Time Personalization
Every user session is personalized in real-time — from homepage layout to thumbnail selection:
| Layer | What's Personalized | Latency Budget |
|---|---|---|
| Homepage Rows | Which categories appear, in what order | <50ms |
| Row Content | Which titles in each row | <100ms |
| Thumbnails | Which frame/artwork shown per title | <20ms (pre-computed) |
| Search Results | Result ranking + autocomplete | <150ms |
| Notifications | What, when, and how to notify | Async (minutes) |
Latency Architecture:
- Pre-computed — Heavy ML models run offline; results cached per-user (refresh every 4 hours)
- Near-real-time — Session signals (what you just watched) update recommendations within 30 seconds
- Real-time — Context signals (time of day, device, mood selection) applied at request time
Conclusion
A content platform NSA is defined by three forces: scale (500M daily streams), personalization (every user gets a unique experience), and global reach (content delivered from 200+ edge locations). The architecture must support all three simultaneously while enabling rapid content experimentation and creator empowerment.
Key Takeaway: In a content platform, the recommendation engine isn't a feature — it's the product. The entire architecture exists to deliver the right content to the right user at the right time. Every other component (CDN, encoding, metadata, search) is in service of that mission.