# Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB
## 1 Introduction  
5G technology provides very attractive features along with artificial intelligence, the Internet of Things, and intelligent robot communication in wireless mobile communication services, and this technology both connects and serves various devices, but also through convergence with other devices. It is an important technology that is improving the convenience of life. In 5G mobile communication environments, among the limitations currently experienced by users, the amount of traffic is increasing at a tremendous rate owing to the increase in multimedia and social network services [1, 2]. In particular, device-to-device (D2D) streaming services under 5G technologies will become a very important technology as they become generalized, and the service type is expected to converge to streaming-oriented services. In 5G-based D2D streaming environments, flow streaming is operated by processing various information requests of D2D user equipment (DUE). In 5G-based wireless mobile networks, DUE and cellular user equipment (CUE) access the wireless network via the base station called the 5G NodeB (gNB), which isconnected by access routers to obtain wireless local area networks (WLANs). For D2D streaming services in this environment, monitoring and detecting large media flow rates are very difficult, and it is an important strategy [3, 4]. Such environments cannot cooperatively utilize the resources and channel bandwidth of the gNB. Sensing and managing flow rates at locations close to the mobile clients is an effective strategy for improving the quality of D2D streaming service. In particular, detecting and managing D2D streaming flows at the proxy cache of the gNB is an attractive approach to reducing network traffic congestion and improving the quality of D2D streaming services, owing to the high demand for network bandwidth and continuous playback constraints of D2D streaming applications. Generally, the upstream traffic from the base station to CUEs is a one-to-many multicast, while the upstream traffic from CUEs to the base station is many-to-one delivery. The forwarding traffic rate from DUE to DUE is one-to-one delivery. Because of the properties of upstream and forwarding traffic, congestion commonly occurs in many-to-one and one-to-one directions. Similarly, the traffic-congestion rates in a gNB can occur in the upstream and D2D direction. The traffic congestion occurring in a gNB is of two types [5; 6]. The first type is node-level congestion, which is common in conventional networks. It is caused by buffer flow in the node and can result in packet loss, and increased queuing delay. A packet loss in turn can result in retransmission, subsequently resulting in the consumption of battery power and resource interference. The second type is link-level congestion, which increases packet service time, and decreases both link utilization and throughput; furthermore, it degrades the quality of service (QoS) for DUEs. Both node-level congestion and link-level directly affect D2D streaming QoS. Therefore, a flow sensing-based congestion detecting scheduler is proposed as an operation to control the flow rates of D2D streaming on a 5G gNB. Existing solutions have entailed algorithms for controlling flow rates such as source rate and peer-to-peer live streaming to solve such problems [7; 8]. The sensing-based rate control mechanism under a 5G gNB channel is performed to differentiate flow streaming services despite wireless link errors and resource interference, since the transport protocol must decrease the flow rate only when the traffic in the network experiences rate inconsistency. Algorithms to control the source rate are typically adopted at the DUE layer to optimize the streaming quality, and they are subject to the bandwidth limit provided by the flow control mechanism and QoS requirements for D2D [9; 10; 11].