# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
###### Abstract  
With the proliferation of video surveillance system deployment and related applications, real-time video analysis is very critical to achieving intelligent monitoring, autonomous driving, etc. Analyzing video stream with high accuracy and low latency through the traditional cloud computing represents a non-trivial problem. In this paper, we propose a non-orthogonal multiple access (NOMA)-based edge real-time video analysis framework with one edge server (ES) and multiple user equipments (UEs). A cost minimization problem composed of delay, energy and accuracy is formulated to improve the quality of experience (QoE) of the UEs. In order to efficiently solve this problem, we propose the joint video frame resolution scaling, task offloading, and resource allocation algorithm based on the Deep Q-Learning Network (JVFRS-TO-RA-DQN), which effectively overcomes the sparsity of the single-layer reward function and accelerates the training convergence speed. JVFRS-TO-RA-DQN consists of two DQN networks to reduce the curse of dimensionality, which, respectively, select the offloading and resource allocation action, as well as the resolution scaling action. The experimental results show that JVFRS-TO-RA-DQN can effectively reduce the cost of edge computing and has better performance in terms of convergence compared to other baseline schemes. mobile edge computing (MEC); non-orthogonal multiple access (NOMA); video offloading; resource allocation; deep reinforcement learning (DRL) 202315848. 12.22.12.  
delay is due to a lack of effective performance. Mobile edge computing (MEC)-based video analysis is the only feasible method to satisfy the demand for a large volume of video streams in real-time. As an emerging computing architecture, MEC can decentralize the computing ability from centralized cloud centers to users and mobile devices, which are close to the edge of the network [3]. Computing the task at the edge of the network can not only reduce the delay and the load of bandwidth, but also reduce the risk of privacy leakage and improve data security. Most of the existing articles on task offloading adopt an orthogonal frequency division multiple access technology (OFDMA) [4], which can only provide a single channel resource for the UEs and has low bandwidth resource utilization. To improve the utilization of bandwidth resources, non-orthogonal multiple access (NOMA) has been proposed [5]. Unlike orthogonal multiple access technology (OMA), NOMA can accommodate more users via non-orthogonal resource allocation, that is, it can provide services to multiple users on the same subchannel at the same frequency and time, thus improving spectral efficiency [6; 7]. Applying NOMA technology to the video offloading process can effectively increase the capacity of the bandwidth resources, reduce the delays of video stream offloading, and improve the user's quality of experience (QoE) [8]. However, real-time video analysis is not easy. Offloading video to the edge for analysis requires high demands on resources and latency, which needs to dynamically balance the transmission and computation processes. Specifically, video analysis usually has a high demand for resources. Analyzing the video frame by frame requires a large amount of computing resources [9; 10], which may easily lead to a long delay [11]. Moreover, the complex network structure necessitates the consumption of several GBs of memory. Due to the limitation of the computational resources of the edge, it is necessary to enact a reasonable offloading decision and resource allocation to satisfy the demand of delay. Generally, the offloading decision optimization problem is always combined with resource allocation, which leads to a non-convex NP problem, where it is difficult to determine the best way to allocate resources in a distributed environment. Recently, deep reinforcement learning (DRL) has been widely used in many applications of mobile communication [12].