Cognitive streaming on android devices

As the number of mobile devices increases, so do the complexity of wireless networks and the user´s requirements. This tendency makes necessary for Multimedia Services to take the needed actions to adapt to the upcoming technology. A prominent example of this type of services is HTTP Adaptive Video Streaming Applications. In this research, we have studied how the latest HTTP Adaptive Streaming techniques, mainly developed for standard computers, could be adapted and used in mobile wireless devices. Furthermore, inspired by these solutions, which usually make use of Reinforcement Learning (RL) algorithms to find the suitable streaming rate, we have conceived a novel smart video player client in Java for Android platform using the Dynamic Adaptive Streaming over HTTP (DASH) protocol. We have assessed the performance of our proposed solution in a self-developed wireless test-bed under different network conditions. Thus, we have seen that by including in the reward function contributions regarding the download speed of the video segments, especially needed due to the fluctuating nature of the wireless networks, and the segments already buffered, improves drastically the overall performance of the video client. Besides that, we have discovered that, in a cognitive adaptive approach, bandwidth constraints affect the user´s experience more substantially, while impairments such as packet loss can be prevented.