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.