# Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB
## 2 Related Work  
Recently, D2D streaming in the 5G-based wireless mobile networks on 5G gNB has attracted significant interest from both academia and industry as an emerging technology for future global mobile systems [2; 12]. Various technologies have been proposed for 5G-based wireless mobile systems, including control of flow and traffic congestion, resource allocation/reuse/sharing, interference-awareness, and power control [15; 16; 17]. In particular, solutions for flow and traffic congestion contribute significantly to improving quality of experience (QoE) and resource efficiency in the D2D application domain, in addition to fairness and responsiveness of the transport protocol. Typically, the 5G-based D2D streaming suffers from high traffic congestion rates owing to flow error in the buffer cache, imbalances in transmission rate, and restrictions in channel bandwidth and resources. Various solutions have been proposed to solve these limitations [4; 16; 18]. [PERSON] et al. proposed a solution to mitigate the inference effects between D2D pairs and CUEs to enhance QoS of D2D communications by reusing spectrum resource allocation [19]. This solution can be improved while ensuring quality of experience (QoE) of CUEs by applying possible uplink/downlink resources of the CUEs. To solve the congestion problem caused by interference, this solution should consider a network based on traffic-flow approximation and nonlinear dynamics for both DUEs and CUEs in links. [PERSON] et al. proposed a solution by applying centralized and distributed algorithms. The centralized algorithm ensures that the CUEs have sufficient probability coverage by preventing interference from D2D pairs [20], and the distributed algorithm ensures that the D2D pairs can maximize the capacity of the spectrum. Although this solution achieves coverage gain, it does not efficiently ensure the QoS for DUEs. [PERSON] et al. proposed an iterative combinatorial auction, in which the spectrum resource is defined as concepts of resource units for creating bids and the D2D. This solution is an algorithm to improve the sum rate for a D2D underlay network by reducing the interference effect between the D2D pairs and CUEs [21]. [PERSON] et al. proposed a resource-allocation algorithm to improve the overall system throughput, while satisfying a particular signal to interference plus noise ratio (SINR) target for both CUEs and D2D groups [22]. [PERSON] et al. proposed a multimedia streaming algorithm using D2D in 5G ultra-dense networks [23]. This solution involves scheduling algorithms for streaming the flow content, using D2D communication. In particular, this algorithm ensures the QoS for live video streaming while reducing the flow error for media streaming. However, this solution does not accurately characterize the properties of flow when a high traffic flow rate is transmitted between D2D pairs. To enhance the throughput and fairness, [PERSON] et al. proposed information theoretic link scheduling [24], and [PERSON] et al. proposed a quantile-based carrier-sense multiple access solution [25]. They focus on optimizing the sum throughput while considering the fairness for DUEs and CUEs. Meanwhile, to enhance the throughput and fairness, and reduce latency and backhaul link congestion, various caching and equation-based solutions have been proposed [26; 27]. The flow traffic in D2D communications is efficient to caching since it requires higher traffic flow rates and provides an imbalanced content reuse property. However, they do not effectively control high flow rates owing to dynamic traffic rates between DUEs and CUEs under a gNB. However, this solution does not accurately characterize the flow when a high traffic flow rate is transmitted between D2D pairs. However, this solution does not fully encompass the nature of a flow when the traffic flow transmission rate between D2D pairs is high. To maintain the D2D streaming quality on a 5G gNB, channel properties such as source rate, flow error, and streaming rate should be considered. [PERSON] et al.