## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Machine Learning (ML)  
Machine learning (ML) is used to predict bandwidth and views as well as increase video streaming bitrate to improve the Quality of Experience (QoE) [14]. Table 5 summarizes the many papers that use machine learning to increase QoE in video streaming applications. The proposed scheme in [11] significantly reduces bandwidth consumption by 45% with less than a 0.1% failure ratio while minimizing performance degradation with Naive linear regression (LR) and neural networks (NN). Next, [PERSON], [PERSON] [52] developed a system called PARSEC (PAnoRamicStrEaming with neural Coding) to reduce bandwidth requirements while improving video quality based on super-resolution, where the video is significantly compressed at the server and the client runs a deep learning model to enhance the video quality. As for this, although [PERSON], [PERSON] [52] successfully reduce the bandwidth requirement and enhance the quality of the video, deep learning is large in models. It also results in the slowest inference rate. Furthermore, [PERSON], [PERSON] [53] present a method for adapting to changing video streams with the combination of the Markov Decision Process and Deep Learning (MDP-DL). In [PERSON], [PERSON] [54], a strategy for adapting to fluctuating video streams (the Reinforcement Learning (RL) model) is researched. Next, a Recurrent Neural Network-Long Short-Term Memory(RNN-LSTM) and Logistic Regression-Ridge Regression(LR-RR) to predict bandwidth and viewpoint is researched by [PERSON], [PERSON] [42] and [PERSON], [PERSON] [55]. To increase QoE, [PERSON], [PERSON] [56] suggested a Q-learning technique for adaptive streaming systems. In [57], the deep reinforcement learning (DRL) model uses eye and head movement data to assess the quality of 360-degree videos. \begin{table}
\begin{tabular}{c c c} \hline
**Source** & **Viewport Prediction Scheme** & **Descriptions** \\ \hline
[48] & k-Nearest Neighbors (k-NN) & Improve the accuracy of traditional linear regression (LR) with cross-users watching behaviors that take advantage of prior users’ data by identifying common scan paths and allocating a higher chance to future FoVs from those users. \\ \hline
[47] & Deep content analysis & Concurrently leverage sensor characteristics (HMD orientations) and content-related information (image saliency maps and motion maps) with LSTM to predict the viewer fixation in the future. The estimated viewing probability for each equirectangular tile may then be used in the quality optimization based on probability. \\ \hline
[49] & 3D-CNN (convolutional neural networks) & 3D-CNN to extract the Spatio-temporal features (saliency, motion, and FoV info) from the videos, has better performance than [47]. \\ \hline
[50] & Content Analysis + Cross-user similarity & PARIMA, which is a hybrid of Passive Aggressive (PA) Regression and Auto-Regressive Integrated Moving Average (ARIMA) times series models to predict viewports based on users’ behavior and the YOLOv3 algorithm on the stitched image to recognize the objects and retrieve their bounding box coordinates in each frame. \\ \hline
[51] & Content Analysis + Cross-user similarity & 2 dynamic viewport selection (DVS) which changes the streaming areas depending on content complexity and user head movements to assure viewport accessibility and non-delay visual views for virtual reality users. To achieve higher accuracy, DVS1 focuses on the adjusted prediction distance between two prediction mechanisms whereas DVS2 selects the tiles for the following segment based on the modified prediction difference between actual and predicted perspectives based on content complexity variations. \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._[PERSON], [PERSON] [58] deploys RAPT360, a reinforcement learning-based Rate Adaptation with adaptable prediction and tiling for 360-degree video streaming, addresses the needs for precise viewport prediction and efficient bitrate allocation for tiles.