Challenge-Example

This repository is an example about the submission of challenge https://2021.acmmmsys.org/rtc_challenge.php. Its zip package(https://github.com/OpenNetLab/Challenge-Example/archive/refs/heads/master.zip) can be directly uploaded as a bandwidth estimator to OpenNetLab platform for this challenge.

Challenge Manual

You need to design and implement a python class Estimator in the file, BandwidthEstimator.py, that is the interface to predict the bandwidth for AlphaRTC https://github.com/OpenNetLab/AlphaRTC#pyinfer.

class Estimator(object):
    def report_states(self, stats: dict):
        '''
        stats is a dict with the following items
        {
            "send_time_ms": uint,
            "arrival_time_ms": uint,
            "payload_type": int,
            "sequence_number": uint,
            "ssrc": int,
            "padding_length": uint,
            "header_length": uint,
            "payload_size": uint
        }
        '''
        pass

    def get_estimated_bandwidth(self)->int:
        return int(1e6) # 1Mbps

Notes

1. The report_states will be called by AlphaRTC core process and to tell the estimator RTC packets information with partial metadata above mentioned.
2. The get_estimated_bandwidth will also be called by AlphaRTC core process to fetch the predicted bandwidth by your estimator.
3. The two interfaces will be called in one thread and maybe get some side-effect if they take a long time to return.
4. The calling frequency of report_states is per RTC packet.
5. The calling frequency of get_estimated_bandwidth is about 200 milliseconds.
6. You can use any built-in library of python 3.6.9 or third-parties libraries we pre-installed in Challenge-Environment.

Submission Verification

# Pull the docker image of challenge environment
docker pull opennetlab.azurecr.io/challenge-env

# Download the configuration and test media
wget https://raw.githubusercontent.com/OpenNetLab/AlphaRTC/main/examples/peerconnection/serverless/corpus/receiver_pyinfer.json -O receiver_pyinfer.json
wget https://raw.githubusercontent.com/OpenNetLab/AlphaRTC/main/examples/peerconnection/serverless/corpus/sender_pyinfer.json -O sender_pyinfer.json
mkdir testmedia
wget https://github.com/OpenNetLab/AlphaRTC/raw/main/examples/peerconnection/serverless/corpus/testmedia/test.wav -O testmedia/test.wav
wget https://raw.githubusercontent.com/OpenNetLab/AlphaRTC/main/examples/peerconnection/serverless/corpus/testmedia/test.yuv -O testmedia/test.yuv

# Run your example locally
docker run -d --network=host --rm -v `pwd`:/app -w /app --name alphartc_pyinfer opennetlab.azurecr.io/challenge-env peerconnection_serverless receiver_pyinfer.json
docker exec alphartc_pyinfer peerconnection_serverless sender_pyinfer.json

If the outvideo.yuv and outaudio.wav are generated at the current folder, it means your bandwidth estimator has connected to AlphaRTC successfully.

If you want to try example in different machine. Pull the docker image and download the configuration and test media in both machine. Then modify the sender_pyinfer.json in sender machine, change "dest_ip" to receiver's IP.

        "sender": {
            "enabled": true,
            "dest_ip": "recever_ip",
            "dest_port": "8888"
        },

Make sure your sender machine can access receiver machine's 8888 TCP port and any UDP ports.

Then run the following command, and check if the outvideo.yuv and outaudio.wav are generated at the receiver machine.

# receiver
docker run -d --network=host --rm -v  `pwd`:/app -w /app --name alphartc_pyinfer opennetlab.azurecr.io/challenge-env peerconnection_serverless receiver_pyinfer.json

# sender
docker run -d --network=host --rm -v  `pwd`:/app -w /app --name alphartc_pyinfer opennetlab.azurecr.io/challenge-env peerconnection_serverless sender_pyinfer.json