Optimized Online Lecture for Narrow-Bandwidth Network

Tingyu Chen and Anandha Gopalan



Abstract:

Education plays an important role in a country's development, and remote education, where barriers of distance can be overcome by information technology and more education resources are thus made available, can be used to improve education accessibility in low-income countries. However, one of the key problems of current distance education approaches, such as Massive Open Online Courses (MOOCs), is the high requirement of bandwidth which is not necessarily available. In this paper, we propose a method of carrying out online lectures in an optimized form where the influence of the low bandwidth on the quality of the lecture is minimized. The proposed method only involves transmission of audio, courseware as static files, and actions encoded as textual data. With replicated state machine theory, the synchronization between all clients is guaranteed. This allows us to achieve lecture delivery with good quality under low-bandwidth network conditions. A prototypical platform for the delivery of such optimized lectures is designed, implemented using Golang and JavaScript, and evaluated.