# Enhancing the Performance of XR Environments Using Fog and Cloud Computing
## 2 Related Works  
VR [10; 11] immerses the users entirely in a virtual world, enabling interactions completely detached from reality. AR [12] enriches everyday experiences by overlaying virtual objects or information onto the real world, often through smartphone applications (apps) or AR glasses. MR combines virtual and real worlds, allowing interactions with virtual objects within the real world. XR technologies are revolutionizing various fields, including gaming, education, healthcare, industrial automation, arts, and entertainment. These technologies continue to evolve and expand.  
XR environments blur the boundaries between the physical and digital realms, overcoming the physical space limitations [13]. This convergence provides new opportunities for users to interact with diverse digital content daily. XR shapes the future of computing and interaction paradigms. Research efforts are currently underway to enable XR experiences in cloud environments, aiming to overcome the constraints related to the physical locations and computational performance.  
Various research topics around cloud-based XR system design and edge computing in XR systems exist. This section introduces these research topics. Research related to cloud-based XR system design [7] focuses on creating virtual environments centered on cloud servers and delivering them to the users. Although these systems can provide high-performance computing and the ability to handle large-scale data, they are constrained by network latency and bandwidth issues, particularly in achieving real-time interactions.  
Research on XR system design using edge computing [14; 15] has explored methods of improving user interactions by deploying edge computers in local environments. Edge computers can handle large-scale data processing and real-time rendering locally, enhancing the user experience. Research on hybrid cloud-edge environments [16] explores methods to combine cloud and edge computing and provide optimal performance and user experiences. In methods involving mobile devices or VR equipment, there are ongoing efforts to address the latency issues, which can be problematic when using direct streaming or edge computing solutions [17; 18]. This study addressed network latency issues, efficiently processing large datasets, and enabling real-time interactions.  
Research on real-time data processing and compression technologies has investigated methods [19; 20; 21] to process and compress large datasets. These technologies reduced the volume of data, minimized network latency, and provided users with faster response times. These research efforts encompassed two approaches for compressing point cloud data. The direct compression approach compressed point cloud data directly using various data structures and compression techniques [19]. Alternatively, researchers have converted raw-point cloud data, such as packet data, into lossless range images. They applied different compression algorithms, such as those used for images and videos, e.g., MPEG, to reduce the volume of data [21]. Both methods aim to reduce the data size while maintaining essential information in point cloud datasets.  
Research on the interactions and user experience has focused on enhancing the user's experience within XR environments [22; 23; 24]. This study evaluated various methods to improve user experience, emphasizing user interactions and feedback processing. These related studies are integral to enhancing XR environments through cloud-based XR systems and edge computing, providing valuable insights and inspiration for these research endeavors.