Hardware Encoder Compatibility Issue with Nvidia Jetson Orin Nano

Issue Overview

The Nvidia Jetson Orin Nano development board lacks hardware encoding capabilities, which has caused significant concern among users. This issue is particularly problematic for those who rely on hardware encoding for tasks such as video streaming, real-time processing, and machine learning applications. Users are experiencing limitations in encoding performance, especially when dealing with high-resolution video streams or when the system is under load from other concurrent tasks.

The absence of hardware encoding is seen as a major drawback compared to previous Jetson models, such as the Jetson Nano, which included this feature. This change impacts various use cases, including:

• Decoding RTSP streams
• Performing inference
• Adding on-screen display (OSD)
• Encoding video
• Streaming video to another RTSP source

The issue is consistent across all Orin Nano boards and significantly affects the system’s ability to handle video processing tasks efficiently.

Possible Causes

1. Hardware Design Change: Nvidia may have removed the dedicated hardware encoding block from the Orin Nano SoC design, possibly to reduce costs or prioritize other features.

2. Software Restriction: There’s speculation that the limitation could be due to software restrictions rather than a hardware omission.

3. Product Differentiation: Nvidia might be intentionally limiting the Orin Nano’s capabilities to differentiate it from higher-end models like the Orin NX.

4. Cost Optimization: The removal of hardware encoding could be a cost-cutting measure to position the Orin Nano at a lower price point.

5. Performance Trade-offs: Nvidia may have prioritized other performance aspects over hardware encoding, assuming that software encoding would be sufficient for most use cases.

Troubleshooting Steps, Solutions & Fixes

1. Utilize Software Encoding:

   • Use CPU cores for software encoding with ffmpeg to achieve 1080p30 encoding.
   • Example command:

     ffmpeg -i input.mp4 -c:v libx264 -preset medium -crf 23 -c:a aac -b:a 128k output.mp4
     

2. Consider Upgrading to Jetson Orin NX:

   • For users requiring higher encoding performance, consider using Jetson Orin NX modules instead of the Nano.

3. Explore External Hardware Encoders:

   • Investigate compatible hardware encoding chips that can be integrated with the Orin Nano on a custom PCBA.
   • Research NVME video encoder accelerators compatible with GStreamer.

4. Optimize Software Encoding:

   • Implement efficient software encoding techniques to maximize CPU utilization.
   • Use multi-threading to improve encoding performance:

     import multiprocessing
     from concurrent.futures import ThreadPoolExecutor
     
     def encode_chunk(chunk):
         # Implement encoding logic here
         pass
     
     with ThreadPoolExecutor(max_workers=multiprocessing.cpu_count()) as executor:
         results = list(executor.map(encode_chunk, video_chunks))
     

5. Reduce Encoding Workload:

   • Lower the resolution or frame rate of the video stream to reduce the encoding burden on the CPU.
   • Implement more efficient pre-processing techniques to reduce the amount of data that needs to be encoded.

6. Monitor System Resources:

   • Use tools like top or htop to monitor CPU usage during encoding tasks.
   • Optimize other processes running on the system to free up resources for encoding.

7. Explore Alternative Encoding Libraries:

   • Investigate GPU-accelerated encoding libraries that might offer better performance on the Orin Nano’s GPU.
   • Consider using NVIDIA’s Video Codec SDK if compatible with the Orin Nano.

8. Community Feedback and Updates:

   • Stay informed about potential software updates or community-developed solutions that may improve encoding capabilities.
   • Participate in NVIDIA developer forums to voice concerns and collaborate on potential workarounds.

9. Custom Hardware Solution:

   • For advanced users, consider developing a custom NVME video encoder accelerator compatible with GStreamer to address the hardware encoding limitation.

10. Provide Feedback to NVIDIA:

    • Submit feature requests and feedback to NVIDIA, emphasizing the importance of hardware encoding for your use case.
    • Engage with NVIDIA support to explore potential future updates or alternative solutions.