In-Network Caching and Edge Computing-Based Live Broadcasting Optimization for Football Competitions

Abstract

The business of football competitions is called the number one sport in the world, thanks to more than one billion people’s attention. With the development of big convergence media, the live broadcasting of football competitions gradually becomes industrialization and commercialization, which has a direct relationship with economic growth. For the live broadcasting of football competitions, the users focus more on quality of experience, i.e., definition and instantaneity. In terms of such two metrics, the current live broadcasting schemes are difficult to cover them well. Therefore, this paper exploits the emerging in-network caching and edge computing technologies to optimize the live broadcasting of football competitions, shorten for IELB. At first, the live broadcasting optimization framework based on in-network caching and edge computing is presented. Then, the auction-based method is used to address the task scheduling problem in the edge computing. In addition, a video compression algorithm based on adaptive convolution kernel is introduced to accelerate the video transmission and guarantee users to obtain the contents of football competitions as quickly as possible. The proposed IELB has been verified based on the collected real football competitions dataset by evaluating response time, and the experimental results demonstrate that IELB is feasible and efficient.