The expansion of the online video content continues in every area of the modern connected world and the need for measuring and predicting the Quality of Experience (QoE) for online video systems has never been this important. This paper has designed and developed a machine learning based methodology to derive QoE for online video systems. For this purpose, a platform has been developed where video content is unicasted to users so that objective video metrics are collected into a database. At the end of each video session, users are queried with a subjective survey about their experience. Both quantitative statistics and qualitative user survey information are used as training data to a variety of machine learning techniques including Artificial Neural Network (ANN), K-nearest Neighbours Algorithm (KNN) and Support Vector Machine (SVM) with a collection of cross-validation strategies. This methodology can efficiently answer the problem of predicting user experience for any online video service provider, while overcoming the problematic interpretation of subjective consumer experience in terms of quantitative system capacity metrics.
With the increasing demand for over the top media content, understanding user perception and Quality of Experience (QoE) estimation have become a major business necessity for service providers. Online video broadcasting is a multifaceted procedure and calculation of performance for the components that build up a streaming platform requires an overall understanding of the Content Delivery Network as a service (CDNaaS) concept. Therefore, to evaluate delivery quality and predicting user perception while considering NFV (Network Function Virtualization) and limited cloud resources, a relationship between these concepts is required. In this paper, a generalized mathematical model to calculate the success rate of different tiers of online video delivery system is presented. Furthermore, an algorithm that indicates the correct moment to switch between CDNs is provided to improve throughput efficiency while maintaining QoE and keeping the cloud hosting costs as lowest possible.
The impact of online video advertisement has an evolving and undeniable influence on the success of online video streaming. A successful online video advertisement campaign deployment necessitates: "targeting appropriate marketing audience, determining optimum intervals to insert advertisement, associating the production quality of the content while considering advertisement conceptual features, matching the relevance of advertisement context to the content theme, calculating the applicable number of ads for stitching into the content, and correlating the ratio of advertisement length to total active watch duration". This paper proposes a novel model for inserting advertisement into online video that considers content and commercial specific properties while optimizing Quality of Experience (QoE) by estimating suitable duration for advertisement, number of splits and content relation. The proposed model has been evaluated in a controlled on-line video test environment so that the success rate of this platform has been compared with the advertisement insertion strategies of technology frontrunners YouTube and Vimeo. In terms of medium and long length online videos, advertisements located within the content provides a better QoE compared to the ones that are located at the beginning of the video. For short length online videos, the general expectation of the audience tends to see the content immediately and any advertisement insertion related delay results in a corresponding customer behavior where 25% tend to quit after 3 seconds and another 25% after 5 seconds.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.