Cognitive Video Streaming

Video-on-demand (VoD) streaming services are becoming increasingly popular due to their flexibility to allow users to access their favorite video contents anytime, anywhere from a wide range of access devices such as smart phones, computers and TV. The content providers rely on highly satisfied subscribers for revenue generation and there has been significant efforts in developing approaches to “estimate” the quality of experience (QoE) of VoD subscribers. But a key issue is that QoE is not defined, appropriate proxies needs to be found for QoE, via the streaming metrics (the quality of service (QoS) metrics) that are largely based on initial startup time, buffering delays, average bit rate and average throughput and other relevant factors such as the video content and user behavior and other external factors. The ultimate objective of the content provider is to elevate the QoE of all the subscribers at the cost of minimal network resources, such as hardware resources and bandwidth. We propose a cognitive video streaming strategy in order to ensure the QoE of subscribers while utilizing minimal network resources. The proposed cognitive video streaming architecture consists of an estimation module, a prediction module and an adaptation module. Then, we demonstrate the prediction module of the cognitive video streaming architecture through a play time prediction tool. For this purpose, the applicability of different machine learning algorithms such as k-nearest neighbor, neural network regression and survival models are experimented with; then, we develop an approach to identify the most relevant factors that contributed to the prediction. The proposed approaches are tested on data set provided by Comcast Cable