Implementation of IP Video Streaming Software to Identify Availability and Audience

Abstract

The perpetual rise of the video on demand is currently one of the leading challenges the telecommunications industry faces. It per passes the eternal comparison with a service that continuously set’s the bar at a highly elevated consumer quality. And the user, advertiser and all stakeholders involved not only are used to it, but demand equal and/or similar value, i.e., Broadcast Television. Such dichotomy has made this relatively new medium create a long list of technologies to make this as viable as possible. However, the solutions only work to a certain extent and critical problems remain not yet addressed. One in particular is delivery assurance in Internet Protocol networks, which affects every stakeholder on these New Media outlets. Keeping this issue in mind, this work developed a range of experiment scenarios through a software-based apparatus in order to convey a technical assessment of key variables in this ecosystem. Afterwards, from these tests an analysis was conducted which brought about a series of discoveries in regards to the technology performance in terms of availability and audience. Ultimately, it culminated in one the central contributions of this research, that is, how to mathematically interpret this type of data indicating its statistical relations. Such methods unveiled impacts and feasibility of: Privacy protocols, mobile and landline connection, latency, delay, loading time, interactions volumes, content history, channel characteristics and user attributes. In other words, this work developed a tool to measure audience and availability in IP delivery. And, it also forged a method to interpret and model the measurements into statistical patterns that can provide predictability. Additionally, it is stated that the significance of this research was confirmed through the Law of Large Numbers, which showed that the data has statistical validity to interpret and envision behavior. That is, the work presents data with a reliability of 97% with a margin of error of 3.03% which confirms this, to the best of our knowledge, as the most comprehensive accurate study of this nature in comparison to the state of the art in the literature. It is also necessary to ascertain that the tool is restricted to measuring quantities related to each content displayed for the largest platform of online video and the most desktop utilized Web Browser, i.e., YouTube and Google Chrome.