Predicting Quality Of Experience For Online Video Systems Using Machine Learning

As the expansion of the online video broadcasting continues in every area of the modern connected world, the need for measuring and predicting the Quality of Experience for content delivery has never been this important. This demo paper has designed and developed a real-time and continuously trained machine learning model in order to predict QoE for online video systems. For this purpose, a platform has been developed where video content is unicasted to a cluster of users simultaneously while objective video metrics are collected into a database. At the end of each video, each user is queried with a subjective survey about their experience. Both quantitative statistics (video metrics) and qualitative information (user surveys) are used continuously as training data to machine learning model. The overall results show that proposed QoE estimation system provides an average Mean Opinion Score (MOS) precision with an error rate ranging from 12% to 15%. This methodology can efficiently answer the problem of predicting user experience for any online video delivery system, while overcoming the problematic interpretation of subjective consumer experience in terms of quantitative metrics

Predicting quality of experience for online video service provisioning

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

On the Modelling of CDNaaS Deployment

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

Load-Balancing for Edge QoE-Based VNF Placement for OTT Video Streaming

© 2018 IEEE. Over The Top (OTT) service providers require platforms to support distributed, complex, cloud-oriented, scalable, micro-service based systems. Such systems require on-the-fly placement of Virtual Network Functions (VNF) to support streaming and transcoding of content based on QoE feedback provided by the end-user. This paper proposes a QoE Scheme to support on-the-fly virtual network functions deployment for OTT video streaming and transcoding. The QoE feedback considers limited cloud resources, transcoding requirements, throughput and latency. Both horizontal and vertical scaling strategies (including VM migration) are discussed to cover up availability and reliability of intermediate and edge Content Delivery Network (CDN) cache nodes

On the Load Balancing of Edge Computing resources for on-line video delivery

Online video broadcasting platforms are distributed, complex, cloud oriented, scalable, micro-service based systems that are intended to provide Over-The-Top (OTT) and live content to audience in scattered geographic locations. Due to the nature of cloud VM hosting costs, the subscribers are usually served under limited resources in order to minimize delivery budget. However, operations including transcoding require high computational capacity and any disturbance in supplying requested demand might result in Quality of Experience (QoE) deterioration. For any online delivery deployment, understanding users QoE plays a crucial role for rebalancing cloud resources. In this work, a methodology for estimating Quality of Experience is provided for a scalable cloud based online video platform. The model will provide an adeptness guideline regarding limited cloud resources and relate computational capacity, memory, transcoding and throughput capability and finally latency competence of the cloud service to QoE. Scalability and efficiency of the system are optimized through reckoning sufficient number of VMs and containers to satisfy the user requests even on peak demand durations with minimum number of VMs. Both horizontal and vertical scaling strategies (including VM migration) are modelled to cover up availability and reliability of intermediate and edge Content Delivery Network (CDN) cache node

Modelling quality of experience for online video advertisement insertion

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. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Supereye: smart advertisement insertion for online video streaming

Without any doubt, state of art advertisement insertion mechanisms along with the requested video content has emerged to be the most crucial part of online video delivery ecosystems. There are several widely deployed methods which have been used since the first days of video streaming such as tag word matching between video content versus advertisement content along with manual matching-based approaches. Conventional but non-scalable and context independent methods cannot fulfil the requirements of an online video platform when there are several millions of user generated videos along with premium content and advertisement of varying production quality. In such environment, a content aware advertisement insertion framework is required based on object recognition, machine learning and artificial intelligence to understand the context of the video and match appropriate advertisement and stitch the advertisement at the most convenient moment of the target video content. In this paper, SuperEye; a deployment ready, content aware, scalable, distributed advertisement insertion framework for a 5G oriented online video platform is designed and developed. The foundational object analyzing mechanism of the underlying system examine each particular context that is part of the wider video and advertisement catalogue using object recognition while generating a time-lapse map of all objects that are detected through the video. Based upon this information, the framework matches the most significant object that is detected for a particular interval and associates the advertisement with similar properties. Additionally, this novel technique does not require any watch history or personalized data related to the user, but primarily interested in only the current requested content information. Therefore, this framework can work along with any type of recommendation engine or rank based association algorithm. The proposed framework is independent of the user information and regarding the subjective user results collected, successful video to ad match ratios of SuperEye significantly exceed the current implementations of YouTube, Vimeo and DailyMotion