## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Viewport-Based Streaming  
In the case of 360-degree video, it would be a waste of network resources to transmit the entire panoramic content as the users typically only see the scenes in the viewport. The bandwidth requirement can be decreased, and transmission efficiency could be improved by identifying and transmitting the current viewport content and the predicted viewport corresponding to the head movement of users. Similar to the tiling technique in the previous section, the server contains a number of video representations that range not just in bitrate but also in the quality of various scene areas. Then, the region of the viewport is dynamically selected and streams in the best quality while the other regions are in lower quality or not being delivered at all to reduce the bandwidth transmission. In other words, the highest bitrate is assigned to tiles in users' viewports, while some other tiles possess bitrates that are proportionate to the likelihood that users may switch viewports, which is also similar to DASH. However, the number of adaption variants of the same content increases dramatically to smooth the viewport-switching due to the sudden head movements. As a result, storage is sacrificed, and the transmission rate increases. [PERSON], [PERSON] [41] proposed a DASH 360\({}^{\circ}\) Immersive Video Streaming Control System which consists of control logic with two cooperating components: quality selection algorithm (QSA) and view selection algorithm (VSA) to dynamically select the demanded video segment. The QSA functions similarly to traditional DASH adaptive video streaming algorithms whereas VSA aims to identify the proper view representation based on the current head position of the users. Ref. [41] reduced segments bitrate around 20% with improved visual quality. In [12], the adaptation algorithm first selects the Quality Emphasized Region (QER) of the video based on the viewport center and the Quality Emphasis Center (QEC) of the available QERs, hence providing high interactive service to head-mounted device (HMD) users with low management. However, improved adaption algorithms are required to predict head movement, as well as a new video encoding approach to do quality-differentiated encoding for high-resolution videos. High responsiveness and processing power are required to adapt to rapid changes in viewports and viewport prediction to ensure smooth viewport switching with accurate prediction. Many viewport prediction approaches have been developed to cover the demands, such as historical data-driven probabilistic, popularity-based, deep content analysis, and so on as summarized in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Viewport Prediction Scheme** & **Descriptions** \\ \hline
[42] & Historical viewport movement & Prediction with Linear Regression (LR) and Ridge Regression (RR) using viewing data collected from 130 users. \\ \hline
[43] & Cross-user similarity & Cross-Users Behaviors (named CUB360) based on k-NN and LR take into account both the user’s specific information and cross-user behavior information to forecast future viewports. \\ \hline
[44] & Popularity-based model & Predict based on the popularity of the tiles where they are visited with a higher frequency at a certain time, might be due to the nature of the video like interesting content along with the evaluation of the rate-distortion curve for each tile. \\ \hline
[45] & Popularity-based model & Similar to [44] and provide the popularity of each shown viewport (heatmap) and rate-distortion function for each tile-representation for the interested segments periodically to clients during each downloading. \\ \hline
[46] & Content Analysis + Popularity & Sensor- and content-based predictive mechanisms, similar to [47] with linear regression (LR). When a transition due to insufficient bandwidth occurs, the tile popularity is solely used to determine the tile quality levels. \\ \hline \hline \end{tabular}
\end{table}