MANSY_ImmersiveVideoStreaming

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MANSY_ImmersiveVideoStreaming

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Implementation of paper "MANSY: Generalizing Neural Adaptive Immersive Video Streaming With Ensemble and Representation Learning" and a trace-driven simulator for immsersive (or, omnidirectional, 360-degree) video streaming.

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# MANSY_ImmersiveVideoStreaming
Implementation of paper "[MANSY: Generalizing Neural Adaptive Immersive Video Streaming With Ensemble and Representation Learning](https://arxiv.org/abs/2311.06812)" (accepted by *IEEE Transactions on Mobile Computing*) and a trace-driven simulator for immsersive (or, omnidirectional, 360-degree) video streaming.

MANSY is short for "ense**M**ble and represent**A**tion lear**N**ing based **SY**stem for tile-based immersive video streaming". MANSY is also named after Duo's beloved fiancée, as a way of expressing gratitude for her generous support towards Duo's research career.

Paper:

```
@article{duo2023mansy,
  title={MANSY: Generalizing Neural Adaptive Immersive Video Streaming With Ensemble and Representation Learning},
  author={Wu, Duo and Wu, Panlong and Zhang, Miao and Wang, Fangxin},
  journal={arXiv preprint arXiv:2311.06812},
  year={2023}
}
```

## Requirements
```
python>=3.8
torch<=2.0
tianshou== 0.4.8
prettytable==3.5.0
numpy==1.24.3
gym==0.26.2
ffmpeg==4.2.2
```
Note: Other environment settings may work, but we didn't test on them.

## Folder Content
- `dataset_preprocess`: This folder contains scripts for preprocessing datasets, such as extracting and simplifying viewport files, extracting video chunk information.
- `viewport_prediction`: This folder contains codes for immsersive video viewport prediction.
- `bitrate_selection`: This folder contains codes for immsersive video bitrate selection.
- `datasets`: This folder contains datasets.
- `models`: This folder contains model checkpoints saved during training. Examples are provided in this folder to better understand the file structure.
- `results`: This folder contains training/testing results files. Examples are provided in this folder to better understand the file structure.

## Uage
### Part 1: Preprocess Dataset
#### Step 1: Download Video and Viewport Datasets

Frist, we need to download datasets and save them in folder `datasets`. For example, let's say we want to download Jin2022 dataset used in our paper. Download it from here: https://cuhksz-inml.github.io/head_gaze_dataset/.

Save the videos in the dataset in the folder `datasets/raw/Jin2022/videos`, and please organize the videos in the following format:
```
datasets/raw/Jin2022/videos/
    video1/  # video{id}
        1-1M.mp4  # {id}-{bitrate_version}.mp4
        1-5M.mp4
        1-8M.mp4
        1-16M.mp4
        1-35M.mp4
    video2/
        ...
```
Save the viewports files in the dataset in the folder `datasets/raw/Jin2022/viewports`, and please organize the files in the following format:
```
datasets/raw/Jin2022/videos/
    1/  # {user_id}
        file1.csv  
        file2.csv 
        file3.csv 
        file4.csv 
        file5.csv 
    2/
        ...
```
Note that if you determine to use the Jin2022 dataset, please kindly cite the following paper:
```
@inproceedings{jin2022you,
  title={Where Are You Looking? A Large-Scale Dataset of Head and Gaze Behavior for 360-Degree Videos and a Pilot Study},
  author={Jin, Yili and Liu, Junhua and Wang, Fangxin and Cui, Shuguang},
  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
  pages={1025--1034},
  year={2022}
}
```
Besides, the original Jin2022 dataset does not contain videos in different bitrate versions, so you need to manually transform them (possibly using ffmpeg).

#### Step 2: Download Bandwidth Dataset

If you want to use the bitrate selection codes, you will also need to download a bandwidth dataset for simulating network conditions. Let's say we want to download `4G` dataset used in our paper. Download it from the following paper:
```
@article{van2016http,
  title={HTTP/2-based adaptive streaming of HEVC video over 4G/LTE networks},
  author={Van Der Hooft, Jeroen and Petrangeli, Stefano and Wauters, Tim and Huysegems, Rafael and Alface, Patrice Rondao and Bostoen, Tom and De Turck, Filip},
  journal={IEEE Communications Letters},
  volume={20},
  number={11},
  pages={2177--2180},
  year={2016},
  publisher={IEEE}
}
```
Save the dataset in the folder `datasets/raw_network/4G`, and please organize the files in the following formats:
```
datasets/raw_network/4G/
    file1
    file2
    file3
    file4
    file5
```
#### Step 3: Process Datasets
Once you complete download and file organization, you can use the scripts in `dataset_preprocess` to process the datasets.

First, change the working directory into `dataset_preprocess`:
```sh
cd dataset_preprocess
```

Run the following command to process viewports/videos/bandwidths datasets:
```sh
python hmdtrace.py  --dataset Jin2022  # process viewports
python video.py --dataset Jin2022  # process videos
python network.py --dataset 4G  # process bandwidths
```

The processed files will be saved at:
```
datasets/Jin2022/viewports/ 
datasets/Jin2022/video_manifests/
datasets/network/4G/
```

#### Add New Datasets
If you want to add a new dataset, in addition to following the above steps, you also need to change the following codes/files:
- `config.yml`: Please add the information of the new dataset here. 
- `dataset_preprocess/hmdtrace.py`: Please specify the way to process the new viewport dataset here.
- `dataset_preprocess/network.py`: Please specify the way to process the new network dataset here.

### Part 2: Viewport Prediction
If you want to play viewport prediction, first change the working directory:
```sh
cd viewport_prediction
```

By simply running the `run_model.py` with some arguments, you can run a viewport prediction model. For example:
```
python run_models.py --model mtio --train --test --train-dataset Jin2022 --test-dataset Jin2022 --his-window 5 --fut-window 15 --bs 512 --seed 5 --dataset-frequency 5 --sample-step 5 --hidden-dim 512 --block-num 2 --lr 0.0001 --epochs 200 --epochs-per-valid 3 --device cuda:0
```
Check file `run_model.py` for the detailed explanation of the command arguments.

You can customize your own models following the example of `run_model.py`.

### Part 3: Bitrate Selection
If you want to play bitrate selection, first change the working directory to `viewport_prediction`:
```sh
cd viewport_prediction
```

Run the `predict.py` to generate files of predicted viewports for the bitrate selection experiments:
```sh
python predict.py --model regression --device cpu --dataset Jin2022 --bs 64 --seed 1
```
You can use other models (with path to the model checkpoints) to generate predicted viewports. Note that this generation can be done once for a lifetime.

Next, change the working directory to `bitrate_selection`:
```sh
cd ..
cd bitrate_selection
```

By simply running the `run_simple_rl.py` with some arguments, you can run a bitrate selection model. For example:
```sh
python run_simple_rl.py --epochs 100 --step-per-epoch 6000 --step-per-collect 2000 --batch-size 256 --train --train-dataset Jin2022 --test --test-dataset Jin2022 --qoe-train-id 0 --qoe-test-ids 0 --test-on-seen --device cuda:0 --seed 1
```
You can customize your own models following the example of `run_simple_rl.py`.

If you want to try *MANSY*, try the following command:
```sh
python run_mansy.py --train --test --epoch 1000 --step-per-epoch 4096 --step-per-collect 4096 --lr 0.0005 --batch-size 512 --train --train-dataset Jin2022 --test --test-dataset Jin2022 --qoe-test-ids 0 1 2 3 --test-on-seen --lamb 0.5 --train-identifier --identifier-epoch 1000 --identifier-lr 0.0001 --device cuda:0 --gamma 0.95 --ent-coef 0.02 --seed 5 --use-identifier
```
Check file `run_simple_rl.py/run_mansy.py` for the detailed explanation of the command arguments.

#### Behavior Cloning (BC) Initialization
Behavior cloning (BC) is a common trick to initialize the deep inforcement learning (DRL) model. Our codes also support BC initialization.

In our codes, we use an MPC-based policy with perfect knowledge of the trainin environment as the expert. We then use the expert to generate a set of demonstrations, which will be used to train the DRL agent with BC.

To use BC, the first step is to generate some demonstrations with the expert:
```sh
python run_expert.py --train-dataset Jin2022 --train --valid --horizon 4 --proc-num 8
```
Check `run_expert.py` for the detailed explanations of the arguments.

Next, we can integrate BC into our tranining pipeline. Let's say pretrain the DRL agent with 150 BC steps:
```sh
python run_mansy.py --train --test --epoch 1000 --step-per-epoch 4096 --step-per-collect 4096 --lr 0.0005 --batch-size 512 --train --train-dataset Jin2022 --test --test-dataset Jin2022 --qoe-test-ids 0 1 2 3 --test-on-seen --lamb 0.5 --train-identifier --identifier-epoch 1000 --identifier-lr 0.0001 --device cuda:0 --gamma 0.95 --ent-coef 0.02 --seed 5 --use-identifier --bc --bc-max-steps 150 --bc-identifier-max-steps 150
```

Note: In our case, we do not find BC to work quite well (with negligible improvement on convergence speed or performance), so we do not report this trick in our paper.

## Citation
If you find this repository useful, please kindly cite our paper:
```
@article{wu2024mansy,
  title={Mansy: Generalizing neural adaptive immersive video streaming with ensemble and representation learning},
  author={Wu, Duo and Wu, Panlong and Zhang, Miao and Wang, Fangxin},
  journal={IEEE Transactions on Mobile Computing},
  year={2024},
  publisher={IEEE}
}
```

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