LAUSR

Wireless multimedia big data contains valuable information on users' behavior, content characteristics and network dynamics, which can drive system design and optimization. The fundamental issue is how to mine data intelligence and further incorporate them into wireless multimedia systems. Motivated by the success of deep learning, in this work we propose and present an integration of wireless multimedia systems and deep learning. We start with decomposing a wireless multimedia system into three components, including end-users, network environment, and servers, and present several potential topics to embrace deep learning techniques. After that, we present deep learning based QoS/QoE prediction and bitrate adjustment as two case-studies. In the former case, we present an end-to-end and unified framework that consists of three phases, including data preprocessing, representation learning, and prediction. It achieves significant performance improvement in comparison to the best baseline algorithm (88 percent vs. 80 percent). In the latter case, we present a deep reinforcement learning based framework for bitrate adjustment. Evaluating the performance with a real wireless dataset, we show that the perceived video QoE average bitrate, rebuffering time and bitrate variation can be improved significantly.