# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 1 Introduction  
Future 6G communication aims to provide a revolutionary user experience [1, 2], with real-time communication holding high expectations in achieving the goals of future mobile communications. On the other hand, the user-perceived capacity is a novel performance metric that has attracted considerable recent attention because it characterizes the quality of experience in mobile multimedia services [3]. In this paper, our objective is to improve the user-perceived capacity to meet the demands of real-time communication in future wireless communications. Real-time communication is essential for applications where latency is a critical factor. It ensures that the time taken for data to travel from the transmitter to the receiver is within acceptable limits, enabling seamless and interactive experiences. Study [4] indicated that communication in real-time systems must be predictable, and a scheme was proposed for providing predictable inter-process communication in real-time systems with a point-to-point interconnection network. Study [5] focused on the time sensitive networks, analyzed the key functional parameters affecting the deterministic behavior in real-time communication protocols and, based on the configuration of these parameters, derived the constraints required for computing schedules to ensure the deterministic end-to-end delay of critical communication flows. In [6], the authors concentrated on real-time communication forindustrial automation environments, aiming to meet the demands of real-time communication in a scenario where the number of networked devices and computational capabilities are expected to significantly increase. The authors of [7] focused on wireless sensor networks, summarizing and employing various methods based on both hard real-time and soft real-time models. Furthermore, they provided scheduling policies to ensure the requirements of real-time communication. In particular, hard real-time communication implies that missing the deadline can lead to a system-wide failure, necessitating the satisfaction of end-to-end latency constraints within the deadline. On the other hand, soft real-time communication aims to reduce the transmission of packets that exceed the deadline, that is, to minimize the probability of exceeding the deadline. In this paper, we focus on hard latency constraints. This is due to hard latency constraints playing an extremely important role in the ultra-reliable and low-latency communications (URLLC) scenario, ensuring timely and reliable data delivery [8]. Many studies have primarily targeted hard latency constraints, such as [9; 10; 11; 12; 13; 14; 15; 16; 17]. In the literature, the authors of [10] considered a communication network under hard real-time constraints, wherein the source node transmits information to the destination node via a directed acyclic graph subject to hard latency constraints. In [9], the authors demonstrated that hard latency constraints can be provided using frequency or spatial diversity and time domain power adaptation, with the condition that diversity gains are at least two. Study [9] also revealed the underlying relationships between the required average power, throughput, and outage probability. The authors of [12] presented a novel power control scheme enhancing the link layer performance in delay-sensitive communications over fading channels. This scheme is particularly adept at managing joint queue length awareness. In [13], the authors introduced an optimal dynamic coding algorithm, along with its low-complexity approximations, designed for deadline-constrained traffic. Additionally, a joint coding and rate control policy proposed by [13] outperformed in time-varying channels, making it particularly beneficial for multimedia streaming and interactive real-time applications. This proposed policy was demonstrated to offer superior performance and stability.