Video Processing Pipeline Architecture

Pipeline Components

A typical video processing pipeline has five components:

┌─────────┐    ┌─────────┐    ┌─────────┐    ┌─────────┐    ┌─────────┐
│  API    │───▶│  Queue  │───▶│ Workers │───▶│ Storage │───▶│ Delivery│
│ Layer   │    │         │    │  (GPU)  │    │         │    │         │
└─────────┘    └─────────┘    └─────────┘    └─────────┘    └─────────┘

API Layer

• Accepts job submissions
• Validates inputs
• Authenticates and authorizes
• Creates job records
• Enqueues work

Queue

• Buffers jobs between API and workers
• Handles priority ordering
• Supports retry logic
• Provides backpressure

Workers

• Pull jobs from queue
• Download input video
• Process on GPU
• Upload output
• Update job status

Storage

• Input staging area
• Output storage
• Signed URL generation
• Lifecycle management

Delivery

• Webhook notifications
• Status API
• Download URLs

Stage Decomposition

Break video processing into logical stages:

Ingest Stage

Input URL → Validate → Download → Stage for processing

Options:

• Direct download: Simple, but blocks GPU
• Pre-stage: Download before worker picks up
• Streaming: Process while downloading

Decode Stage

Video file → Extract frames → Extract audio

Considerations:

• Codec support (H.264, HEVC, VP9, AV1)
• Resolution and framerate detection
• HDR metadata handling

Process Stage

Frames → AI model inference → Enhanced frames

This is where GPU compute happens:

• Upscaling (Real-ESRGAN)
• Face restoration (GFPGAN)
• Denoising
• Color correction

Encode Stage

Enhanced frames → Encode → Package with audio

Output optimization:

• Platform-specific encoding (YouTube, TikTok, etc.)
• Quality/size tradeoffs
• Hardware encoding when available

Deliver Stage

Output file → Generate signed URL → Notify customer

Queue Design

The queue is the heart of the pipeline:

Queue Selection

• Redis: Fast, simple, good for single-region
• RabbitMQ: Feature-rich, good for complex routing
• SQS: Managed, auto-scaling, AWS-native
• Pub/Sub: Managed, global, GCP-native

Priority Queues

Different priority levels for different tiers:

High priority queue   → Enterprise customers
Medium priority queue → Pro customers
Low priority queue    → Free tier

Dead Letter Queues

Jobs that fail repeatedly go to a DLQ for investigation:

• Automatic after N retries
• Manual inspection and replay
• Alerting on DLQ depth

Visibility Timeout

Jobs must be invisible while processing:

• Set timeout longer than max processing time
• Extend timeout periodically for long jobs
• On failure, job returns to queue after timeout

Worker Architecture

Workers are the GPU-powered processing units:

Worker Lifecycle

1. Start up, initialize GPU and models
2. Poll queue for jobs
3. Process job
4. Report completion
5. Return to polling

Model Loading

AI models must be loaded into GPU memory:

• Load on startup (fast first job)
• Keep in memory between jobs
• Lazy load rarely-used models

Graceful Shutdown

Workers must shut down cleanly:

1. Stop accepting new jobs
2. Complete current job
3. Update job status
4. Release queue lock
5. Exit

Health Checks

Monitor worker health:

• GPU memory usage
• Jobs processed per hour
• Error rate
• Average processing time

Reliability Patterns

Idempotent Processing

Same input + same parameters = same output:

• Allows safe retries
• Prevents duplicate charges
• Enables result caching

At-Least-Once Delivery

Jobs may run more than once:

• Job completion is idempotent (re-running updates, doesn't duplicate)
• Output overwrites, doesn't append
• Webhooks can be deduplicated by job ID

Circuit Breakers

Stop sending jobs to failing workers:

• Track failure rate per worker
• Open circuit at threshold (e.g., 50% failures)
• Periodic probe to close circuit

Bulkheads

Isolate failures:

• Separate queues per customer tier
• Separate worker pools per region
• Resource limits per job