## 3 A Service-Less Multicast Architecture for Distance Learning
### EI Enabled Multicast Architecture  
The pandemic has forced the world to lock down and disrupted normal life patterns including education. However, video call applications such as Zoom and MS teams accessible via wired and wireless technologies including cellular networks have facilitated online education. Most of the students taking classes at various levels, specifically at the school and college level, live within close vicinity. For instance, most of the school students live in the same city or neighborhood as the school. Moreover, most of the classes in different schools are scheduled at the same time interval, placing a considerable burden on the RAN, backhaul, and transit links for video transmissions. As the mode of communication is unicast in existing 5G architecture, many fold redundant resources are consumed at each network layer. Multicast is a feasible and inherent solution for such use cases, where most of the students are expected to watch the same stream for a long time. The existing VNF based multicast initiation module can be utilized for optimization of distance learning traffic. However, the application of EI is imperative to answer: **(a)** which unicast sessions can be merged in a multicast session based on parameters from UE and MNO, and **(b)** where edge caching of distance learning session can help users with low QoE to view content at a latter time. VNF based Multicast can be aided with machine learning and edge caching techniques for a proactive approach towards MNO resource optimization and user QoE enhancement. An overview of the stated concept is illustrated in Figure 2. The details of our system design are presented below. * **Cold start:** The main objective of cold start is to introduce multicast without the service announcement from the service provider. We utilize the service-less multicast architecture to enable cold start multicast for distance learning. In the basic service-less multicast architecture, the online sessions are started with unicast. As users keep on joining the session, the unicast delivery is converted to multicast enabled by the VNF module. However, the cold start has several disadvantages such as: **(a)** It takes a little time to convert from unicast to multicast and **(b)** while transmitting unicast, many resources are wasted until conversion and convergence. Moreover, we collect data regarding user behavior for proactive and predictive multicast and caching in the cold start stage. * **Predictive multicast:** In the second stage, EI is proposed to identify opportunities to proactively create a multicast session beforehand as soon as the first request arrives at the server. Proactive multicast is enabled by applying ML to group users based on their geographic locations and session times among other attributes. Unsupervised learning (clustering) techniques will be used on the collected data to group users in the same session without obtaining information from the video streaming application. The MNO may face penalties due to the unnecessary initiation of the multicast service and usage of multicast resources in case only a single request arrives. * **Proactive Caching:** In the third stage of our proposed design, proactive caching will be used to store the recorded streams near users. The distance learning applications such as MS Teams provides access to recorded streams that can be viewed later. The user behavior may vary towards the attendance of live session based on his interest and channel conditions. Our proposed EI will learn user access patterns from their preferences to optimize edge caching. Edge caching will minimize backhaul and RAN resource usage.