## 5 Discussion and Future Scope  
With the advancement of 5G technology, AR/VR requires significant resources for real-time processing, i.e., rendering, vision, and physics engines, at end user devices to maintain its ubiquitous nature across heterogeneous networks, including 4G, 5G, and WiFi. Usually, in mission-critical networks, the delivery of small information packets (32 to 200 bytes) within a latency of 1 ms [112] results in a huge computation delay with the traversal of each packet undergoing the procedure of processing, queuing, and transmitting through multiple routers, where each router contributes 10 ms of computation delay [14]. However, the adoption of high-end hardware and processing approaches such as the Multi-Path Transmission Control Protocol (MPTCP) [113] to reduce the computational complexity and transmission instability can only reduce the latency by 5-6 ms for time-sensitive AR/VR applications. These challenges highlight the need for innovative solutions to enable ultra-low-latency and reliable transmission for real-time updating across 5 GB networks [114]. Though existing networks are able to offer significant enhancements for rich content delivery, they cannot meet the demand for up to a 5.2 Gbps data rate per user and an end-to-end RTT of 1 ms [115]. The requirement of achieving \(<\)1 ms for the transmission of information from the total end-to-end RTT on the networks is still a challenging task as the latency between end users continues to increase with distance. As traditional communication systems focusing mostly on error-free transmission cannot satisfy the transmission rate requirements of AR/VR due to the limitation of Shannon's capacity, we require a semantic communication approach to achieve end-to-end latency through a novel transmission protocol or algorithm. Considering the above-mentioned requirements, we propose a semantic-communication-based delay-aware algorithm, which adopts an intelligent routing protocol to extract the semantic level features such as \((i)\) the selection of the most-optimal or the shortest path to ensure the instant packet delivery of prioritized information and \((ii)\) the accurate prediction of the routing decision by evaluating the subflow's delivery performance in sending prioritized content with the lowest latency. With the motivation to improve the traffic capacity requirement, which plays an integral role in the 6G era, our proposed approach can be capable of identifying/predicting the accurate route for the transmission of selected information while maintaining a low-latency communication [116]. With the integration of AI/ML techniques similar to a few existing wireless B5G networks [117; 118] to increase the transmission rate beyond Shannon's capacity, we introduced an AI/ML integrated learning algorithm to enable the proposed end-to-end semantic communication approach. To implement delay-aware semantic communication involving a proactive transmission strategy of data packets for high delivery rates of video frames (VFs), interference-aware directional beamforming technology is an essential tool to generate highly optimized directional beams. Moreover, this approach requires some preprocessing steps to overcome the bandwidth limitations when handling packet overloading [119], which causes complications in the external sensing environment. By utilizing optimized directional beams as the preprocessing steps followed by the transmission strategy, the selection of a suitable spectrum becomes necessary to handle the recent bandwidth congestion. To overcome the problem of spectrum congestion due the explosive growth of data traffic generated by mobile devices and users, a new license-free optical spectrum, known as visible light communication (VLC), can be envisioned [120; 121]. VLC, known for its secure transmission compared to traditional radio frequency (RF) communications, can be enabled through a setup of optical wireless interfaces at the HMDs.