240 lines
6.5 KiB
Markdown
240 lines
6.5 KiB
Markdown
# Display System Optimization Roadmap
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## Phase 2: GStreamer Integration
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### Overview
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Implement GStreamer-based display backend for hardware-accelerated rendering, particularly optimized for embedded systems like Raspberry Pi.
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### Goals
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- **Performance**: 60-80% reduction in CPU usage
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- **Hardware Acceleration**: Utilize GPU/VPU when available
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- **Zero-Copy**: Minimize memory bandwidth usage
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- **Flexibility**: Support multiple output formats (window, file, network stream)
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### Implementation Plan
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#### 1. Core GStreamer Backend (`src/display/gstreamer_backend.rs`)
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```rust
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pub struct GStreamerDisplay {
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pipeline: gst::Pipeline,
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appsrc: gst_app::AppSrc,
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bus: gst::Bus,
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config: DisplayConfig,
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stats: DisplayStats,
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}
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impl DisplayBackend for GStreamerDisplay {
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// Implement trait methods using GStreamer pipeline
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}
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```
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**Key Features:**
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- Dynamic pipeline creation based on output requirements
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- Hardware encoder detection and utilization
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- Efficient buffer management with memory pools
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- Error handling and pipeline state management
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#### 2. Pipeline Configurations
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##### Basic Window Display
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```
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appsrc ! videoconvert ! videoscale ! xvimagesink
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```
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##### Hardware-Accelerated (Raspberry Pi)
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```
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appsrc ! v4l2convert ! video/x-raw,format=NV12 ! v4l2h264enc ! h264parse ! v4l2h264dec ! xvimagesink
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```
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##### Network Streaming
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```
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appsrc ! videoconvert ! x264enc ! rtph264pay ! udpsink host=192.168.1.100 port=5000
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```
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##### File Recording
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```
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appsrc ! videoconvert ! x264enc ! mp4mux ! filesink location=output.mp4
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```
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#### 3. Dependencies and Setup
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**Cargo.toml additions:**
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```toml
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[dependencies]
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gstreamer = "0.20"
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gstreamer-app = "0.20"
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gstreamer-video = "0.20"
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[features]
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gstreamer-display = ["gstreamer", "gstreamer-app", "gstreamer-video"]
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```
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**System Dependencies:**
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```bash
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# Ubuntu/Debian
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sudo apt install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
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# Raspberry Pi additional packages
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sudo apt install gstreamer1.0-omx gstreamer1.0-plugins-bad
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```
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#### 4. Buffer Management Optimization
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```rust
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struct BufferPool {
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pool: gst::BufferPool,
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allocator: gst::Allocator,
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}
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impl BufferPool {
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fn get_buffer(&self, mat: &opencv::Mat) -> Result<gst::Buffer> {
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// Zero-copy conversion from OpenCV Mat to GStreamer Buffer
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// Use memory mapping when possible
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}
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}
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```
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#### 5. Auto-Detection and Fallback
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```rust
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pub fn create_optimal_display(config: DisplayConfig) -> Box<dyn DisplayBackend> {
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if gstreamer_available() && hardware_acceleration_available() {
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Box::new(GStreamerDisplay::new(config))
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} else {
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Box::new(OpenCVDisplay::new(config))
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}
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}
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```
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### Hardware-Specific Optimizations
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#### Raspberry Pi 4/5
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- **GPU Memory Split**: `gpu_mem=128` in `/boot/config.txt`
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- **V4L2 Codecs**: Utilize hardware H.264 encoder/decoder
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- **DMA Buffers**: Use DMA-BUF for zero-copy operations
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#### NVIDIA Jetson
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- **NVENC/NVDEC**: Hardware encoding/decoding
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- **CUDA Integration**: GPU-accelerated image processing
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#### Intel Systems
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- **VAAPI**: Video Acceleration API support
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- **Quick Sync**: Hardware encoding acceleration
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### Performance Targets
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| Metric | Current (OpenCV) | Target (GStreamer) | Improvement |
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|--------|------------------|-------------------|-------------|
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| CPU Usage | 60-80% | 15-25% | 60-75% reduction |
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| Memory Bandwidth | 2-3 GB/s | 0.5-1 GB/s | 50-75% reduction |
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| Latency | 3-5 frames | 1-2 frames | 60% reduction |
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| Power Consumption | High | Low | 30-50% reduction |
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### Implementation Timeline
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#### Week 1-2: Foundation
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- [ ] Set up GStreamer Rust bindings
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- [ ] Create basic pipeline structure
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- [ ] Implement DisplayBackend trait
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- [ ] Basic window display working
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#### Week 3-4: Optimization
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- [ ] Buffer pool implementation
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- [ ] Zero-copy Mat to Buffer conversion
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- [ ] Hardware acceleration detection
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- [ ] Pipeline optimization
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#### Week 5-6: Advanced Features
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- [ ] Multiple output support (file, network)
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- [ ] Error recovery and robustness
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- [ ] Performance profiling and tuning
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- [ ] Documentation and examples
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#### Week 7-8: Integration and Testing
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- [ ] Update demos to use GStreamer backend
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- [ ] Raspberry Pi testing and optimization
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- [ ] Benchmark comparison with OpenCV
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- [ ] Production readiness assessment
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### Compatibility Matrix
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| Platform | GStreamer Support | Hardware Accel | Recommended Pipeline |
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|----------|------------------|----------------|---------------------|
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| Raspberry Pi 4/5 | ✅ | V4L2 | v4l2convert + omx |
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| NVIDIA Jetson | ✅ | NVENC/NVDEC | nvvidconv + nvenc |
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| Intel x86 | ✅ | VAAPI | vaapi + intel-media |
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| Generic Linux | ✅ | Software | videoconvert + x264 |
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| macOS | ✅ | VideoToolbox | vtenc + osxvideosink |
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| Windows | ✅ | DirectShow | d3d11 + mf |
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### Testing Strategy
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#### Unit Tests
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- Pipeline creation and teardown
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- Buffer management
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- Error handling
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#### Integration Tests
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- End-to-end display functionality
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- Performance benchmarking
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- Memory leak detection
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#### Hardware Tests
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- Raspberry Pi 4/5 validation
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- NVIDIA Jetson compatibility
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- Various resolution and framerate combinations
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### Risk Mitigation
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#### Potential Issues
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1. **GStreamer Version Compatibility**: Different distributions have different GStreamer versions
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2. **Hardware Driver Issues**: Proprietary drivers may not be available
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3. **Memory Alignment**: OpenCV Mat and GStreamer Buffer memory layout differences
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#### Solutions
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1. **Runtime Detection**: Probe capabilities at startup
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2. **Graceful Fallback**: Fall back to OpenCV backend if GStreamer fails
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3. **Memory Copy Fallback**: Use memory copy if zero-copy fails
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### Success Metrics
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#### Must-Have
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- [ ] 50%+ reduction in CPU usage on Raspberry Pi
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- [ ] Maintain visual quality parity with OpenCV
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- [ ] No memory leaks during extended operation
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- [ ] Graceful error handling and recovery
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#### Nice-to-Have
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- [ ] Network streaming capability
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- [ ] Recording to file functionality
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- [ ] Multi-window support
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- [ ] Real-time performance monitoring
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### Documentation Requirements
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- [ ] API documentation for new backend
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- [ ] Hardware setup guides for different platforms
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- [ ] Performance tuning recommendations
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- [ ] Troubleshooting guide for common issues
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- [ ] Migration guide from OpenCV backend
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---
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## Phase 3: Future Enhancements
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### WebRTC Integration
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- Real-time streaming to web browsers
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- Low-latency remote monitoring
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### Custom Shaders
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- GPU-accelerated overlay rendering
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- Advanced visual effects
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### Multi-Display Support
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- Simultaneous output to multiple displays
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- Picture-in-picture functionality
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---
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*This roadmap will be updated as Phase 2 development progresses.* |