###### Abstract  
The COVID-19 pandemic has affected the world socially and economically changing behaviors towards medical facilities, public gatherings, workplaces, and education. Educational institutes have been shutdown sporadically across the globe forcing teachers and students to adopt distance learning techniques. Due to the closure of educational institutes, work and learn from home methods have burdened the network resources and considerably decreased a viewer's Quality of Experience (QoE). The situation calls for innovative techniques to handle the surging load of video traffic on cellular networks. In the scenario of distance learning, there is ample opportunity to realize multi-cast delivery instead of a conventional unicast. However, the existing 5G architecture does not support service-less multi-cast. In this article, we advance the case of Virtual Network Function (VNF) based service-less architecture for video multicast. Multicasting a video session for distance learning significantly lowers the burden on core and Radio Access Networks (RAN) as demonstrated by evaluation over a real-world dataset. We debate the role of Edge Intelligence (EI) for enabling multicast and edge caching for distance learning to complement the performance of the proposed VNF architecture. EI offers the determination of users that are part of a multicast session based on location, session, and cell information. Moreover, user preferences and network's contextual information can differentiate between live and cached access patterns optimizing edge caching decisions. While exploring the opportunities of EI-enabled distance learning, we demonstrate a significant reduction in network operator resource utilization and an increase in user QoE for VNF based multicast transmission. edge intelligence; video multicast; distance learning; eMBMS; edge caching +
Footnote †: journal: _Sensors_  
0000-0001-9000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-000000-00000-00000-00000-000000-00000-000  
The contiguous nature of the pandemic has forced the shutdown of workplaces, public gatherings, and educational institutes disrupting normal social behaviors. Due to the quick spread of new variants and the drastic increase in the number of cases in the second wave, lock-down, and social distancing policies have been implemented while educational institutes have resorted to distance learning all over the world. Network services have experienced a major surge in traffic due to many consequences of the pandemic including work from home, distance learning, telehealth, home-based entertainment, and sports live-streaming events. Modern 4G/5G networks have to support the increasing traffic demands with limited infrastructure upgrades. Moreover, students, in particular, may reside in areas with limited network access or congested dormitories disrupting the conduct of distance learning [2]. Innovative techniques are called for to improve resource utilization of cellular networks in the current pandemic [3]. The major part of the Internet traffic (60-80%) comprises video content. Cisco predicted that, by the year 2021, the mobile video traffic will be seven times higher than today, and the live video traffic will be at least 13 percent of the total Internet traffic [4; 5]. The percentage of live video traffic is predicted to increase in the current scenario of distance learning adopted by teaching institutions around the globe. The statistics of university campuses with distance learning platforms show a huge variation in different Internet traffic patterns [6; 7]. In particular, the number of MS team video calls has jumped from tens daily before the lockdown to hundreds daily after the lockdown. The studies observed that the incoming traffic to campus LAN decreased significantly while outgoing traffic increased 2.8 times due to in-campus hosting of online education platforms.