Serverless Video Processing Pipeline with AWS MediaConvert
The video platform needed a scalable, cost-effective way to handle variable encoding workloads, from quiet periods with few uploads to peak times with hundreds of simultaneous jobs.
Discuss Your Project
The Challenge
Fixed-capacity encoding infrastructure was either over-provisioned (costly) or under-provisioned (slow):
- Encoding workloads were highly variable and unpredictable
- Peak periods could see 100x normal volume during content launches
- Running dedicated encoding servers 24/7 was expensive during quiet periods
- Job failures needed automatic detection and retry without manual intervention
Our Solution
We implemented a serverless encoding pipeline using AWS Lambda triggers and AWS MediaConvert for elastic, pay-per-use video processing.
Architecture
- Trigger: AWS Lambda function monitoring S3 upload events
- Encoding: AWS MediaConvert with partner-specific job templates
- Messaging: ActiveMQ/STOMP for asynchronous job status updates
- Monitoring: NestJS encoder backend tracking job progress
- Storage: AWS S3 for input/output assets
Pipeline Flow
- S3 Event - Video upload triggers Lambda function
- Job Configuration - Lambda reads partner profile and constructs MediaConvert job
- Submission - MediaConvert job submitted with appropriate output settings
- Progress Tracking - STOMP messages relay status to encoder backend
- Completion - Output assets stored in S3, metadata updated in MongoDB
- Error Handling - Failed jobs queued for retry with exponential backoff
Key Features
- Zero Idle Cost - Lambda and MediaConvert charge only for actual usage
- Elastic Scaling - Handles 1 to 1000+ concurrent encoding jobs
- Partner Templates - Pre-configured MediaConvert job templates per partner
- Event-Driven - S3 events automatically trigger encoding workflows
- Comprehensive Monitoring - Job status, duration, and error tracking
Results
Technology Stack
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