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📼 Broadcast SDI Capture System

The Problem

Professional broadcast environments need to capture SDI video feeds in real-time and deliver files that editors can start working with immediately—not after lengthy transcoding. Standard tools either lack proper codec support, produce incompatible file formats, or require manual post-processing.

The challenge: capture broadcast-quality DNxHD video from Blackmagic SDI devices directly to MXF OP1a format on Apple Silicon Macs, with live editing capability.

The Solution

I built a custom FFmpeg-based capture system that compiles FFmpeg 7.1 with DeckLink support for Apple Silicon (M1-M4). The system captures 1080i50 SDI video with embedded timecode and PCM audio, writes partial indexes for live editing, and outputs broadcast-standard MXF OP1a files compatible with Adobe Premiere Pro.

Key Features

  • Real-time SDI capture — DNxHD 120 Mb/s encoding with proper field ordering
  • Live editing capability — Partial index writing allows editors to start working within 30 seconds
  • Broadcast compatibility — MXF OP1a format, DNxHD codec, embedded timecode
  • System validation — Automated testing to verify capture pipeline integrity
  • Automated launcher — macOS application bundle for one-click operation

Technical Architecture

SDI Source → Blackmagic Device → FFmpeg (DeckLink) → DNxHD 120 → MXF OP1a → SSD
             (UltraStudio)        (arm64 compiled)     (1080i50)   (54GB/hour)

Technologies Used

  • FFmpeg 7.1 — Custom compilation with DeckLink SDK for Apple Silicon
  • Blackmagic DeckLink SDK — SDI device integration and timecode handling
  • DNxHD codec — Professional broadcast compression (120 Mb/s)
  • MXF OP1a container — Industry-standard broadcast format
  • Shell scripting — Automation, validation, and system integration

Implementation Details

Build Process

The system requires compiling FFmpeg from source with specific flags for Apple Silicon and DeckLink support. This took research into codec compatibility, container format specifications, and hardware driver integration.

Performance Requirements

  • Storage throughput: 54GB/hour (15MB/s sustained write)
  • Capture latency: <100ms from SDI input to file write
  • Field ordering: Proper interlaced video handling (top field first)
  • Index writing: Partial metadata for live editorial workflows

Real-World Use Cases

  1. Live broadcast archival — Continuous SDI capture for compliance recording
  2. Multi-camera ingest — Parallel capture from multiple SDI sources
  3. Proxy-free workflows — Edit-ready files without transcoding delays

Challenges & Learnings

Challenge: Apple Silicon Compatibility

Standard FFmpeg builds lack DeckLink support for arm64. Solution: Custom compilation with SDK integration and architecture-specific flags.

Challenge: Live Editing Requirements

MXF files need complete indexes before playback. Solution: Implemented partial index writing during capture using FFmpeg’s -write_index flag.

Challenge: Broadcast Format Compliance

Wrong container/codec combinations fail in professional tools. Solution: Researched MXF OP1a specifications and validated with Adobe Premiere Pro.

Results

The system runs in production environments, capturing hundreds of hours of broadcast content. Editors can start working 30 seconds after capture begins, eliminating post-processing bottlenecks.

File size: 54GB/hour at DNxHD 120 Mb/s Format compatibility: Adobe Premiere Pro, Avid Media Composer, DaVinci Resolve Reliability: Zero dropped frames in sustained 8-hour captures

  • GitHub Repository: capture-mxf-system
  • Documentation: Full setup guide, validation scripts, troubleshooting

What This Project Demonstrates

This project shows systems thinking, automation, and infrastructure work—the same skills that translate to data engineering. Just like building reliable video pipelines, data pipelines require:

  • Understanding data flow architecture
  • Optimizing for throughput and latency
  • Ensuring format compatibility across systems
  • Building validation and monitoring
  • Automating repetitive processes

The difference? Data engineering operates at larger scales with more interesting tools. But the fundamentals are the same: move data reliably, solve for scale, automate everything.

Built with FFmpeg, Blackmagic SDK, and a lot of shell scripting. Runs on Apple Silicon.