BeagleBoard/GSoC/2020 Projects/Media IP Streaming
- 1 Proposol of equipping the Beaglebone AI with Media IP Streaming capabilities
- 2 Status
- 3 Proposal
- 3.1 About you
- 3.2 About your project
- 3.3 Misc
This project will equip the Beagleboard AI with Media IP Streaming capabilities, by porting the sound card drivers for CTAG face2|4 Audio Card and the AVB protocol stack from BeagleBone AVB to the BeagleBone AI.
Progress and Documentation/Research Results: https://niklaswan.github.io/GSoC-Overview
This project is currently just a proposal.
All requirements have been fullfilled, the Pull Request can be found here #139
School: Kiel University of Applied Sciences
Primary languages: German, English
Typical work hours: 8AM-5PM CET
Previous GSoC participation: I want to participate at GSoC because I want to gather experience in working within an open source community and try to apply theoretical knowledge into the practical domain. Also I hope to learn new awesome things. This would be my first time participating in GSoC.
About your project
Project name: Media Ip Streaming
The BeagleBone AI is equipped with a high amount of processing power due to the Dual Core ARM Cortex-A15 chip as a main computing unit and its accompanying co-processors. This makes the AI a perfect fit for highly demanding applications regarding CPU consumption, like audio applications which have extremely strong realtime constraints. Professional audio/video studios have to guarantee for small latencies when transmitting media signals between different devices and different media channels in a transmitted stream need to be synchronized. Latency and snychronicity are both extremely important when transmitting e.g. a video channel together with the accompanying audio channel. Those two channels have to be transmitted in a manner, that lip synchronicity can be guaranteed because humans are extremely sensitive to voice offset to accompanying video signals.
To bring media ip streaming capabilities to the BeagleBone AI, the following steps are planned: A previous GSoC project ported a sound card driver from the BeagleBone Green/Black to the BeagleBoard-X15 (https://summerofcode.withgoogle.com/archive/2016/projects/5351212496977920/). This port will now be ported to the BegleBone AI. With the sound card driver successfully ported, the next step would be to port the AVB protocol driver stack from BeagleBone AVB enabling media streaming over the network. This would allow to use the BeagleBone AI as a media streaming device in professional audio/media applications and bring audio stream synchronization features to the BeagleBone AI. Thus allowing for tight synchronization between different audio and video streams which are transmitted over the network. Additionally for people who don't own the CTAG Face 2|4 cape HDMI audio output should be realized.
Practical Use: AVB is primarily used in large scale media productions, like sports venues, broadcasting studios or concert halls. Basically AVB can be used everywhere where media data has to be transmitted over larger distances in a local network. Implementing this on a BeagleBone AI would allow for a low cost alternative for proprietary hardware and further allow for customization by the Beagleboard.org community.
Provide a development timeline with a milestone each of the 11 weeks and any pre-work. (A realistic timeline is critical to our selection process.)
|Mar 30||Proposal complete, Submitted to https://summerofcode.withgoogle.com|
Proposal accepted or rejected Community Bonding Period starts.
|June 1||Pre-work complete, Coding officially begins!|
|June 8||Milestone #1, Introductory YouTube video, review of existing drivers for ctag face audio interface, identifying challenges for porting drivers to Beagle AI and selection of appropriate kernel
|June 15||Milestone #2 Implementation / porting of ALSA audio drivers for ctag face to Beagle AI --> toolchain setup, driver adoptions, coding
|June 22||Milestone #3 Port of sound card drivers, testing, performance check
|July 3 18:00 UTC||Milestone #4 (Phase 1 evaluations), finalzing port of ctag face audio card driver to Beagle AI and getting pull request to Beagleboard Mainline
|July 10||Milestone #5 Review of existing AVB network driver architecture for real-time audio streaming, basis is https://elinux.org/BeagleBoard/GSoC/2017_Projects#Project:_BeagleBone_AVB_Stack , identifying challenges for porting to Beagle AI
|July 17||Milestone #6 Implementation / porting of ALSA AVB network drivers to Beagle AI --> toolchain setup, driver adoptions
|July 24||Milestone #7 AVB ALSA drivers implementation for Beagle AI
|July 31 18:00 UTC||Milestone #8 (Phase 2 evaluations), Getting ALSA AVB network drivers finished and document everything till now
|August 3||Milestone #9 Joining AVB ALSA drivers with ctag face audio card drivers
|August 10||Milestone #10 Performance and integration testing of driver ports
|August 17||Milestone #11, Completion YouTube video, pull request of driver architecture for mainline
|August 24 - 31 18:00 UTC||Final week: Students submit their final work product and their final mentor evaluation|
|August 31 - September 7 18:00 UTC||Mentors submit final student evaluations|
Experience and approach
During my bachelor's degree in information technology I had several courses like programming in C, programming in C++, operating systems and embedded system programming which layed down the basis for developing embedded software. Due to my additional bachelor's degree in audio production I have additional experience in audio applications and audio and media codecs, which will help me to understand the theory behind the different needed algorithms. With my previous development work for the Strämpler project I already have experience in working on complex embedded C projects and which potential pitfalls could occur.
If I get stuck and my mentor is not around I will follow the following steps in displayed order:
- Search the internet for the problem.
- Serach through literature acquired during milestone #1.
- Ask in the GSoC IRC, if fellow students know a solution to the specific problem.
- If the problem is still not solved, postpone the problem until mentor is available again and work on another part of the project.
Equipping the BeagleBone AI with media ip streaming capabilities would allow the Beagleboard.org community to use those capabilities to implement the system in professional media applications. The community could also implement further media protocols like AES/Ravenna to allow the usage of the AI for even more media streaming tasks.
Link to pull request #139.
-  „4.3. PTP — Processor SDK Linux Documentation“. https://software-dl.ti.com/processor-sdk-linux/esd/docs/06_02_00_81/linux/Industrial_Protocols_PTP.html (accessed March 30, 2020).
-  1733-2011 IEEE Standard for Layer 3 Transport Protocol for Time-Sensitive Applications in Local Area Networks. ///.
-  M. A. Yoder und J. Kridner, BeagleBone cookbook, First edition. Sebastopol, CA: O’Reilly Media, Inc, 2015.
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-  A. Liberal de los Ríos, Linux driver development for embedded processors: Learn to develop Linux embedded drivers with kernel 4.9 LTS, Second edition. .
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-  „The Linux Kernel documentation — The Linux Kernel documentation“. https://www.kernel.org/doc/html/latest/index.html (accessed March 26, 2020).
- is used by the AVB protocol to achieve synchronization between devices
- based on IEEE 1588v2
- up to nanoseconds accuracy
- sharing timestamps over the network for synchronization of devices
- uses master/slave hierarchy
- slave retrieves time from master ==> network dely has to be taken into account
- normally endpoint of the network
- single port
- BMCA (best master clock algorithm) determines which clock is used as master (the one with the highest accuracy)
- is used as an endpoint master and has extremely high accuracy (normally timed by GPS or NTP)
- there can be more than one in a network to achieve redundancy
- mutli port
- a network switch with master/slave ports
- accounts for queuing delays when a standrad switch is used and thus improves accuracy
- calculate network delay End-To-End
- no need of PTP equipment but this results in added cost in accuracy
- calculate network delay Peer-To-Peer
- results in high accuracy, but all devices in the network need to be PTP enabled