###### Abstract
With no immediate medical cure in sight, the global community is adopting online tools for education, health, business meeting, and conferences. To fairly address the bandwidth and Quality of Experience (QoE) requirements of live video streams, the emerging 5G networks need novel architectural designs [8; 9]. Live video data-sets show that groups of users request the same content while residing in close vicinity [10]. Distance learning in the pandemic is an example of this situation. Each user receives a separate unicast video stream of the same content towards the RAN resulting in resource wastage. The QoE of the video streams is reduced due to overloaded network resources in the 5G RAN. The multicast delivery can reduce network resource wastage and increase user QoE. However, the identification of unicast streams that are part of live video traffic and their convergence to multicast is necessary. The convergence of edge computing with AI termed as Edge Intelligence (EI) advocates the application of intelligent caching and routing in 5G networks. The integration of AI in edge computing comes with the increasing hardware capabilities, the sophisticated wireless network properties, and the capability of AI to optimize objectives in complex network environments [11; 12]. ML techniques in general, and EI specifically, are called for the identification of multicast video patterns in a service-less 5G multicast architecture [13; 14]. The evolved Multimedia Broadcast Multicast Service (eMBMS) in LTE-A is a service-oriented architecture requiring pre-announcement of the video to enable multicasts. However, the live video streaming and distance learning applications can not follow a service-oriented architecture as, for each stream, the session has to be announced on each Mobile Network Operator (MNO) core. On the contrary, a service-less architecture to enhance the capabilities of live video streaming with a Virtual Network Function (VNF)-based solution has been proposed [4]. A VNF Application Server (VNF-AS) can be instantiated in an MNO core to act as a proxy for end-user and content provider (CP) and instantiate multicast service spontaneously. VNF-AS extends the capabilities of eMBMS to provide service-less multicast and reduce redundant video streams over RAN and core network. We propose VNF-AS to be complemented with the EI and capabilities of mobile edge caching to further alleviate the burden on the 5G cellular network in the context of distance learning. A group of users in the distance learning session are expected to watch recorded streams as they may be absent due to network outages. Moreover, users can experience high SNR leading to low QoE. Caching at the edge of the network can reduce redundant file access from CDNs and cloud data centers for such users. However, the user access patterns, mobility, and network conditions at the network edge need to be predicted [15; 16]. Moreover, ML techniques can also provide intelligent input to the VNF-AS to optimize the decision of what, when, and where to multicast video streams based on user access patterns and content features [13; 17]. Caching is essential for users of distance learning with limited network connectivity. EI can enhance the performance of edge caching with user statistics and content features as input [13; 18]. The main objective of this article is to identify the opportunities of multicast and caching in the existing 5G architecture while evaluating the performance of VNF-AS for multicast distance learning. The contributions of this article are as follows:  
* We propose a service-less multicast architecture enabled by VNF-AS for distance learning. We investigate the proposed VNF-AS for distance learning on a real-time dataset for traffic, QoE, and resource optimization. * We propose and debate the opportunity of EI to enhance the performance of VNF-AS.