ABSTRACT
An enormous increase in the volume of multimedia data has been observed, floating around over the internet in recent times. Production of such massive data is mainly due to the easy access to mobile and usage of other high-end Internet of Things (IoT) devices for different services by a large section of mobile users. Services such as video streaming and other multimedia generate petabytes and terabytes of data to the cloud and are bandwidth-intensive. These services demand managing and processing voluminous data, fast reaction time, and improved quality of service with low network latency in real-time, which is a critical task. Good quality of multimedia services is available with cloud computing, but this comes with long processing times. There are also newer technologies like fog and edge computing, which offers storage and managing of resources at a network edge closest to the users, achieving lower latency than the cloud, which maintains sustainable computing and ensures privacy protection.
The potential, features, and certain recent research challenges of edge computing are highlighted in this chapter to develop a scalable architecture for managing and processing multimedia data. This chapter also presents a review of proposed edge architectures including Mobile Edge Computing, which aims to create a latency efficient mobile computing platform. Machine Learning, Deep Learning, and Peer-to-Peer networking are also discussed as a solution for applying data analytics and distributed file sharing at edges. The chapter explores the overall concept of edge computing to enthusiastic researchers working with challenges in this area and designing architecture that manages multimedia content at edges.
