Video Analytics

Low-Latency Cross-Platform Desktop streaming App for AI Video Analytics

Enterprise-grade Cross-Platform Desktop streaming App, achieving 4-7% CPU usage and 150-200MB memory footprint for HD streaming, with comprehensive telemetry and sub-second recovery, enabling AI Video Analytics across Windows and Android devices.

Video Streaming Analytics

The Challenge

A leading AI Video Analytics company needed a sophisticated cross-platform streaming solution that could capture and transmit desktop content with exceptional performance. The challenge was to develop a robust application that could handle realtime high-definition streaming while maintaining minimal resource usage across diverse hardware configurations and network conditions.

Specifications

  • Enable AI-powered video analytics by ensuring low-latency streaming
  • Stream HD video with minimal resource usage
  • Support multiple screen shares per device
  • Work reliably in variable network conditions

Our Solution

We developed a high-performance streaming solution that leverages platform-specific optimizations and hardware acceleration to deliver exceptional performance with minimal resource overhead. Our implementation focuses on reliability, efficiency, and comprehensive monitoring capabilities.

Implementation Highlights

  • Custom adaptive streaming handling VP8/VP9 codec variations across Android OEMs
  • Hardware-accelerated encoding with platform-specific optimizations
  • Efficient memory management via Rust ownership model
  • WebRTC state machine with automated recovery mechanisms
  • Comprehensive telemetric instrumentation across the application stack

Technical Deep-Dive

System Architecture

Technical Architecture Diagram

High-level architecture showing the interaction between core system components

Tech Stack

Core

Rust + Tauri

Frontend

React + TypeScript

Platforms

Windows & Android

Monitoring

Custom telemetry + Grafana

Platform-Specific Optimizations

  • Android media projection capabilities mapped across API versions
  • Hardware encoder detection and fallback strategies
  • Memory-mapped frame buffer management
  • Battery optimization through efficient threading

Real-World Performance

  • Low latency video streaming
  • 4-7% CPU for HD streaming
  • 150-200MB memory footprint
  • Network recovery < 1 second
  • Multiple simultaneous HD streams

Monitoring Infrastructure

Performance metrics instrumented include:

  • CPU/Memory usage per stream
  • Video encoding performance
  • WebRTC Connection stats
  • Crash Analytics

Business Impact

Our solution delivered exceptional performance while maintaining minimal resource usage, enabling seamless AI video analytics integration and improving overall customer satisfaction through reliable operation and quick issue resolution.

  • Runs on existing customer hardwareStream HD video with minimal resource usage
    CPU Usage: 4-7%, Memory: ~200MB, Battery: YouTube comparable
  • Support multiple screen shares per deviceLow resource consumption allowed for multiple HD streams from single device
  • Work reliably in variable network conditionsAdaptive Bitrate implemented to respond to realtime network conditions
    Network Recovery: <1s, Error Resolution: Automated, Uptime: 99.9%+
  • Enable AI-powered video analyticsLow latency WebRTC stack enabling real-time Video AI
  • Rapid issue resolutionComprehensive telemetry across different components enabling efficient customer support
  • 24x7 ReliabilityApplication is designed to run continuously with automated recovery
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"This implementation showcases our expertise in complex cross-platform development, demonstrating our ability to deliver high-performance solutions that scale."
NextEnterprise-scale AI Agentic Platform for PhroneticAI

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