## 1 Introduction  
Long-term evolution (LTE) and 5G, which were recently launched and which officially triggered the new era of fifth generation telecommunication, [1] are high-speed wireless communication standards for mobile devices in the field of telecommunications that provide high-speed mobile networks for people's lives. LTE and 5G provide mobile users with the stability of data transmission through handover technology, allowing users to have enough bandwidth to meet the needs of quality of service (QoS) for various network applications. In order to meet the increasing demand for data traffic, the deployment of small base stations will play an important role in future network deployment. Network operators must not only meet the needs of mobile users for traditional signal coverage but must also meet the needs of users to provide a quality of experience (QoE) for different network services. When users are moving around the deployment environment of multiple base stations, the user equipment (UE) in general only decides the time to handover based on the signal strength. The most important issue in the handover mechanism is to find the appropriate moment to trigger the handover process. The original event A3 handover mechanism is usually used as a measurement for the required signal strength of handover in LTE, which detects the offset when the signal strength of the neighboring eNB is continuously greater than the signal strength from the serving eNB during the signaling period. However, the handover mechanism should be predicted and carried out before a user encounters QoE degradation. The QoE of network services will be degraded due to the low signal quality. Therefore, it is the motivation of this research to predict the optimal handover time for each UE based on the signal strength provided by neighboring base stations (BSs), and to maintain the required QoE during its continuous movement.  
In addition, real-time video streaming service is a crucial application, such as automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) in the smart machin ery. For instance, real-time video streaming services in UAVs are now widely used for aerial photography, surveillance, image recognition, object detection, and remote sensing. However, existing LTE handover solutions are unable to meet the quality requirements in terms of quality of service for real-time video streaming while the UAV is moving in flight.  
This research proposes a deep learning model on a mean opinion score (MOS) evaluation approach for a real-time video conference service for optimal handover time prediction to improve the user's QoE. We will construct an experimental and simulation platform for LTE/5G networks to analyze the signal strength (reference symbol received power, RSRP) of neighboring base stations reported by UE. Besides the fact that the experimental results show the proposed deep-learning-based MOS-aware handover mechanism, we will analyze the QoE performance and compare the experimental results with the general LTE Event A3 handover mechanism.  
The rest of this paper is organized as follows. Section 2 introduces related research studies. In Section 3, MOS modeling for video conferences over mobile networks is presented. Section 4 presents the handover dataset collection and deep-learning model performance analysis. Section 5 describes the proposed deep-learning-based handover mechanism. Section 6 provides experimental results and performance analysis. Finally, we draw the conclusion and suggest future works in Section 7.