the overall system constraints. To be optimal in the context of a multi-user MIMO system, we need a different approach to the physical layer beamforming and coding design. The downlink of the MU-MIMO channel is known in the information theoretic context as the Broadcast Channel (BC). Several methods have been proposed to improve data rates over the broadcast channel. The Dirty Paper Coding (DPC) concept [10] for non-causally known multi-user interference at the transmitter was proven to eliminate the interference perfectly without power penalty. The main drawback of this method is that finding the resulting optimal transmit co-variances has very high computational complexity. To reduce this computational complexity, several other beamforming techniques have been proposed. Zero-Forcing (ZF) beamforming was introduced as a simple alternative to DPC. In [11], the relationship between the linear ZF precoding design and generalized inverses in linear algebra was presented. Typically, the ZF method is sub-optimal. However, it has been shown to suffer a constant loss relative to capacity achieving approaches such as DPC in the case of large numbers of users and a high signal-to-noise ratio [12; 13]. To overcome the shortcomings of ZF beamforming, where transmission to non-orthogonal users suffers significant power penalty, the proper scheduling and grouping of packets are required [13] to achieve optimal behavior. Indeed, the work in [7; 13; 14] proposed opportunistic or greedy grouping of users. This is indeed a special case of the general problem of joint scheduling and beamforming. This problem is known to be NP-hard [9]. Several approaches have been taken in order to propose scheduling with lower complexity beyond the greedy solutions above. Convex approximation algorithms were proposed in [9]. In the case of large numbers of MSs, opportunistic policies achieve near DPC performance in terms of the average delay [7]. Different policies handle the power constraint issue [15; 16], trying to maximize the overall throughput. Other approaches to maximize the throughput used an arbitrary set of orthogonal beamforming in the case of low-rate beamforming feedback [17]. In [5], partitioning the users into groups with the approximately similar channel co-variance eigenvectors method was presented. This method enables the \"massive MIMO\" gains. In [8], a unified urgent weight was proposed by taking into account QoS requirements, such as delay deadline, minimum data rate, Queue State Information QSI, and the user fairness requirement. Various methodologies have been used to approach the aforementioned joint optimization task in the downlink of MU-MIMO communication systems [18]. In [17], Per User Unitary and Rate Control (PU2 RC) was analyzed, and in [19], a generic channel covariance-based beamforming scheme was presented. In [4], the authors described a method to maximize the system utility, subject to the diverse QoS requirements of users and the power constraints. In [20], Joint Opportunistic Scheduling and Receiver Design (JOSRD) was analyzed for MU-MIMO. All these studies, considered scheduling policies to improve the resource block allocation while maximizing the SINR or the achievable overall system throughput. However, improving capacity is not enough to support new applications. These techniques cannot be applied when both deadlines and constraints are required. While other network management techniques exist for other network models such as device-to-device interference management [21] or ad-hoc networks [22], these techniques are inapplicable in the context of base-station scheduling. Many IoT systems are assumed to be hard real-time systems. In hard real-time systems, if a packet fails to be delivered before its deadline expires, it is considered to be lost. The hard real-time systems problem has been widely discussed in queuing theory [23; 24; 25; 26]. The Earliest Deadline First (EDF) scheduling policy is one of the most common methods to schedule packets in a hard real-time environment [25].