## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Comparison between Techniques  
Firstly, the DASH framework, tiling and viewport-adaptive techniques are correlated to each other as most of the tiling and viewport-adaptive techniques are using the DASH framework. Some of the tiling techniques [12; 34; 37; 38] and the viewport-adaptive approach [45; 48; 49] are all using DASH to stream the areas covered by users' FOV in high quality while some other tiles are streamed in lower quality. The differences between these techniques are the mapping projection, encoding, tiling scheme and tile selection algorithm. However, there are several limitations to the tiling and viewport-adaptive method. Firstly, more bandwidth is required to stream a screen-size movie at viewport devices as compared to a typical 2D laptop screen at the same quality. As illustrated in Figure 5, streaming a viewport region with a width of 110 degrees is still significantly wider than a normal laptop screen with a width of 48 degrees roughly [3; 14]. Furthermore, most tiling solutions employ the viewport-driven technique, in which only the viewport that is the viewed area of the viewer is streamed in high resolution, yet it may also suffer from a significant delay due to the switching of the viewport, which might be due to the video content from the other viewports are not being delivered at the moment. So, when the user \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & \multicolumn{2}{c}{**Technique**} & **Scope** \\ \hline
[11] & \(\bullet\) & Naive linear regression (LR). & Motion detection and prediction. \\  & \(\bullet\) & Neural networks (NN). & Reduce bandwidth requirement and \\  & \(\bullet\) & Deep neural-aware adaptive bitrate (ABR) algorithm (§IV). & Improve video quality. \\ \hline
[53] & \(\bullet\) & Markov Decision Process-Deep Learning (MDP-DL). & Improve Variable bitrate (VBR). \\ \hline
[54] & \(\bullet\) & Reinforcement Learning (RL) model. & Improve Adaptive VR Streaming. \\ \hline
[42] & \(\bullet\) & Logistic Regression-Ridge Regression (LR-RR). & Viewpoint prediction and Bandwidth prediction. \\ \hline
[55] & \(\bullet\) & Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM). & Viewpoint prediction and Bandwidth prediction. \\ \hline
[56] & \(\bullet\) & Q-Learning Reinforcement Learning (RL). & Improve constant bitrate (CBR). \\ \hline
[58] & \(\bullet\) & Markov decision process (MDP). & Viewpoint prediction and Optimal bitrate allocation. \\ \hline
[59] & \(\bullet\) & Encoder-Decoder Based LSTM Model. & Viewpoint prediction and Rate \\  & \(\bullet\) & Model Predictive Control (MPC)-based rate adaptation. & adaptation. \\ \hline
[60] & \(\bullet\) & Markov Decision Process (MDP). & Reactive caching and Viewport \\  & \(\bullet\) & Deep-Q-Network (DQN). & prediction. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Machine learning (ML)-based approaches. abruptly switches his/her viewport during the display time of the current video segment, a delay occurs. Next, as human eyes have a low delay and error tolerance, any viewport prediction errors can cause rebuffering or quality degradation and result in a break of immersion and poor user Quality of Experience (QoE). Furthermore, to accommodate users' random head movements, causing the need to increase the number of tiles of the video has and thus the video size increases significantly. Therefore, the implications of smooth viewport switching, minimized delays, with lessened video size and bandwidth should be addressed during 360-degree video delivery. DASH, tiling and viewport-adaptive are focused on improving the streaming efficiency of the 360-degree video with lower bandwidth by streaming the demanded region of the 360-degree video with higher quality. On the other hand, Machine Learning (ML) techniques not only focus on lowering the bandwidth but also focusing on the improvement of QoE of the streaming.