NetABR: Advancing Multistream Fairness via Bilevel Joint Adaptive Bitrate and Bandwidth Allocation

This is a python implementation of NetABR.

Requirements

Install the required packages using the follow command. Conda environment is recommended.

pip install -r requirements.txt

Running NetABR

The main function is in /train.py. You can use the following command to run NetABR.

python train.py

Parameter Settings

• These are the parameters during the training process.

--save-dir: Directory to save the results during the training process.

--seed: Seed to initialize the neural network parameters.

--total-epoch: Total number of epoch to be trained.

--video-size-file-dir: Path to video size files.

--val-trace-dir: Directory of the trace data.

--k: Number of clusters during pre-processing.

--time-slot: Decision frequency ratio between upper and lower levels.

--device-type: Type of terminal devices participating in experiment.

• These are the parameters during the evaluating process.

--pretrained-abr: Whether to load the ABR model.

--pretrained-abr_dir: Directory of the ABR model.

--pretrained-net: Whether to load the Net model.

--pretrained-net-dir: Directory of the Net model.