Hybrid model for validating performance of streaming video signals

As more and more consumers access streaming video content over the internet, enterprises across the entire video distribution value chain experience tremendous pressure to deliver better performance and high quality of experience (QoE) to the end users. Enhanced video performance is highly desirable, starting from the Video Origin Servers through the core network and Content Delivery Networks (CDN) till the last mile Consumer Premises Equipment (CPE) and Omni-channel device platforms. In addition to the huge surge in streaming video demand in recent years, a growing trend of high audience churn-out to competitors, increases the necessity to assure high video quality at any cost for a largely global consumer base. Hence, there is an urgent need to devise an optimum performance analysis solutions for digital streaming video signals considering all major factors related to end user perception as well as growing concurrent load on the content servers. It is often noticed, that performance analysis of specialized programs like video is done using simulation based methodologies or highly scaled down real user testing techniques, which don't truly represent the real world scenarios. Moreover many dynamic factors like user traffic, concurrency, network bandwidths, packet delay, device types etc. have a significant impact on video performance. Due to these factors the complexity of the test environment and test conditions increases significantly. Hence, in order to provide a realistic solution for analyzing performance of streaming video content, we present a hybrid methodology in this paper by leveraging unique workload models and proven test automation techniques. This hybrid model is found to yield highly accurate assurance levels for performance analysis of streaming video programs.