A Context-aware adaptive algorithm for ambient intelligence DASH at mobile edge computing

Adaptive streaming has recently emerged as a technology enabling high-quality streaming at various bitrates. One of the video streaming challenges remains in research topic nowadays that is choosing optimal segment base on network characteristics and streaming devices, such as network bandwidth, latency, the computational capacities of devices. Researchers have proposed many algorithms to overcome such issues within their predefined conditions. However, those proposed methods do not perform efficiently in the heterogeneous network today. Consequently, in this article, we present research on a context-aware adaptive algorithm for ambient intelligence dynamic adaptive employing mobile edge computing (MEC). Specifically, we apply deep learning in the adaptive algorithm which is installed at the MEC to assist clients in choosing the optimal streaming segments as well as reduce network latency. Furthermore, we apply the multilayer perceptron classifier with data obtained from various experiments of adaptive streaming algorithms then combine them in a general algorithm. In the analysis, we use network simulator NS3 as a tool to carry out the verification of our proposed method. As a result, the proposed research reduces network latency as well as improve quality streaming compared to existing approaches.