Performance of Q-Learning algorithms in DASH

Abstract

Q-Learning is an important class of stochastic optimization which has recently been used in the area of dynamic adaptive streaming over HTTP (DASH). Though DASH is very popular method of video delivery in recent years it is plagued with problems when multiple players share a bottleneck link. Thus, this area has become a very active area of research. Two works which implement Q-Learning in DASH are selected and their performances compared against the Conventional DASH player. It is shown that Q-Learning works well for various network conditions.