ANALYSIS AND ENHANCEMENT OF RESOURCE-HUNGRY APPLICATIONS

Resource-hungry applications play a very important role in people's daily lives, such as real-time video streaming applications and mobile augmented reality applications.
However, there are several challenges to satisfy the user Quality-of-Experience (QoE) requirements of resource-hungry applications. First, these applications usually require a vast amount of network bandwidth resources to support the data communication of different functionalities. However, only limited network bandwidth resources can be assigned to these applications which leads to long network latency and poor user QoE. In addition, artificial intelligent (AI) and machine learning (ML) models are widely adopted in these applications which significantly increases the computation complexity of these applications. Because of the limited computing resource on mobile devices, computation-intensive tasks are offloaded to edge servers located at the edge of the core network. However, additional network latency and bandwidth usage are introduced which may degrade user QoE. In this dissertation, the characteristics of popular resource-hungry applications are first analyzed. Then, based on the analyzed characteristics, we propose several specifically designed algorithms to enhance the performance of several popular resource-hungry applications.