Qualcomm Linux sample apps – building blocks for AI inference and video in IoT applications (Part 1 of 2)

Our previous post in this series introduced you to Qualcomm^® Linux^® software, that lets you write once and use for many Qualcomm IoT system-on-chips (SoCs). We’ve released 22 initial sample apps in the Qualcomm Intelligent Multimedia SDK, which is based on Qualcomm Linux. We’ll blog about four of those apps so you can see the out-of-box experience you’ll have when developing on Linux for our IoT chipsets.

In this post we’ll explore the first two building-block applications.

1. Multi-camera streaming

The command-line application gst-multi-camera-example demonstrates streaming from two camera sensors simultaneously. It can apply side-by-side composition of the video streams to show on a display device, or it can encode and store the streams to files.

The application pipeline looks like this:

The application supports two configurations:

1. Composition and display – The qtimmfsrc plugin on camera 0 and camera 1 captures the data from the two camera sensors. qtivcomposer performs the composition, then waylandsink displays the streams side by side on the screen.
2. Video encoding – The qtimmfsrc plugin on camera 0 and camera 1 captures the data from the two camera sensors and passes it to the v4l2h264enc plugin. The plugin encodes and compresses the camera streams to H.264 format, then hands them off for parsing and multiplexing using the h264parse and mp4mux plugins, respectively. Finally, the streams are handed off to the filesink plugin, which saves them as files.

Here’s an example of the output from the first configuration: Right side image is monochrome, as second camera sensor on development kit is monochrome.

When would you use this application?

gst-multi-camera-example is a building block for capturing data from two camera sensors, with options for either composing and displaying the video streams or encoding and storing the streams to files. You can use this sample app as the basis for your own camera capture/encoding applications, including dashcams and stereo cameras.

2. Video wall – Multi-channel video decode and display

The command-line application gst-concurrent-videoplay-composition facilitates concurrent video decode and playback for AVC-coded videos. The app performs composition on multiple video streams coming from files or the network (e.g., IP cameras) for display as a video wall.

The application can take multiple (such as 4 or 8) video files as input, decode all the compressed videos, scale them and compose them as a video wall. The application requires at least one input video file, in MP4 format with an AVC codec.

The application pipeline looks like this for 4 channels:

Each channel uses plugins to perform the following processing:

• Reads compressed video data from a file using filesrc.
• Demultiplexes the file with qtdemux.
• Parses H.264 video streams using h264parse.
• Decodes the streams using v4l2h264dec.

The decoded streams from all channels are then composed together using qtivcomposer and displayed using waylandsink.

Here’s an example of using the app gst-concurrent-videoplay-composition on 4 video streams:

When would you use this application?

With gst-concurrent-videoplay-composition you can decode multiple compressed video streams, then compose them into a video wall; for example, in retail spaces and digital signage. As an edge box for video surveillance, you can capture input from multiple IP cameras and display it in a single screen. In a video conferencing application, you can process and display feeds from multiple people on the call, with each participant streaming a video.

Next steps

You can get those applications or the entire Qualcomm Intelligent Multimedia SDK on GitHub. And then you can start incorporating them to your own applications.

In the part 2 of this blog, we explore two applications based on the building blocks we’ve described above:

• Seeing through AI: Live stream object detection
• Parallel AI fusion: Four AI inferences on live camera
  
  
  What else is trending for Internet of Things:
  
  Read how CyberLink ports FaceMe to Qualcomm Hexagon NPU for facial recognition on edge devices
  
  How Capgemini uses Qualcomm Dragonwing portoflio to enhance  railway safety with Edge AI
  
  Learn how to optimise your AI model for the Edge
  
  Browse highlights from Embedded World 2025
  Watch developer Build Along sessions:
  
  MCP IoT Agent for Snapdragon X Elite and Rubik Pi
  
  Using TensorFlow to accelerate models on Qualcomm IoT devices
  
  Docker and Qualcomm Dragonwing RB3 Gen 2 x FoundriesFactory
  
  AMA session with Qualcomm and Edge Impulse
  
  AprilTag and Qualcomm RB3 Gen 2
  
  What’s next?
  
  
• Join our Discord community to keep up with our ongoing outreach to developers like you who work with Qualcomm Technologies’ products. 
  
  On Discord, you can rub elbows with our experts, connect with fellow developers working with our technology and keep your fingers on the pulse of developer-focused news and product updates.