Predicting quality of experience for online video service provisioning

Bulkan, Utku; Dagiuklas, Tasos

doi:10.1007/s11042-019-7164-9

Cited by 12 publications

(10 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To increase accuracy, it adds a preprocessing block to extract appropriate features and feeds them into the prediction model. Yet another example is [17] which uses measured video metrics and users' survey data to estimate QoE. Data is collected experimentally including objective metrics such as total stall duration and the number of stalls for each video session.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Video QoE With Deep Learning From Multivariate Time-Series

Dinaki

Shirmohammadi

Janulewicz

et al. 2021

IEEE Open J. Signal Process.

View full text Add to dashboard Cite

The end users' satisfactory Quality of Experience (QoE) is a fundamental criterion for networked video service providers such as video-on-demand providers (Netflix, YouTube, etc.), cloud gaming providers (Google Stadia, PlayStation Now, etc.) and videoconferencing providers (Zoom, Microsoft Teams, etc.). To know the QoE, providers today typically predict it from the Quality of Service (QoS) parameters or the client-side's actual QoE metrics measured at the current time-step. But the former does not precisely reflect the users' experience, and the latter has a delay between QoE measurements at the client-side and the user's current experience. Mitigating this delay can provide a noticeable improvement in the delivery system's performance. For example, accurate forecasting of QoE for the near future allows the service management system to take a proactive approach and fix delivery issues before they become a noticeable problem at the end user, or at least reduce overall QoE degradation. QoE forecasting can also be used in rate adaptation in DASH or resource allocation in wireless networks. In this paper, we propose a method to prognosticate QoE metrics. Using data collected from an industry video streaming testbed for three different classes, we define a multivariate time series forecasting problem. We then model a hybrid state-of-the-art deep learning method, BiLSTM-CNN, to forecast the QoE metrics in advance. Evaluation of our proposed method compared to four other well-known ML models of Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Long short-term memory (LSTM), and Bidirectional LSTM (BiLSTM) demonstrates the superior performance of our proposed method.

show abstract

Section: Related Workmentioning

confidence: 99%

Forecasting Video QoE With Deep Learning From Multivariate Time-Series

Dinaki

Shirmohammadi

Janulewicz

et al. 2021

IEEE Open J. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Under the modelling and pattern mining, the Mean Opinion Score (MOS) model for over the top content is given as Eqn. ( 1) where x denotes the number of product purchase and t is the time since last purchase and g represents the memory parameter sometimes set at a typical value as 0.14 [18]. preparing data for modeling, estimation, validating, scoring data, or related mining activities which leverage data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data and to make predictions about the future.…”

Section: A Overall Analytics Proceduresmentioning

confidence: 99%

Towards Machine Learning Based Analysis of Quality of User Experience (QoUE)

Nwakanma¹,

Hossain²,

Lee³

et al. 2020

IJMLC

View full text Add to dashboard Cite

Industries use various platforms to receive feedback from users of their products. In this paper, there is an overview of the potentials of using natural language processing system (NLP) in classifying the quality of user experience. The user experience is captured using google form. To test the efficacy of the platform, sentiments of users were analysed using hotels.ng as the source of data. The natural processing of electronic word of mouth (e-WOM) can be applied to any feedback platforms to classify and predict customers' sentiments and provide a veritable opportunity for companies to capture the quality of users' experiences and improve service delivery. The feature or sentiments extraction was done using opinion mining and data cleaning tools on heterogeneous data sources to judge the decision-making process of users. Using charts and correlations, with an average performance level of the willingness to recommend and degree of review helpfulness, the platform showed that the Quality of User Experience (QoUE) of the customers are 7.31 and 7.03 respectively. Finally, an improved logistic regression classifier was developed to test, train and classify the user experiences. Comparing the improved logistic regression classifier with standard logistic regression classifier shows that the training accuracy of the proposed improved logistic regression gave 97.67% as against the standard logistic regression which had accuracy of 86.01%

show abstract

“…In order to gather information from users, an online video platform has been developed [33]. The platform is capable of streaming a wide range of online video content with a collection of ads alongside that can be dynamically stitched into watch session.…”

Section: Online Video Platform For Advertisement Insertionmentioning

confidence: 99%

Modelling Quality of Experience for Online Video Advertisement Insertion

Bulkan

Dagiuklas

Iqbal

2020

IEEE Trans. on Broadcast.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Predicting quality of experience for online video service provisioning

Cited by 12 publications

References 20 publications

Forecasting Video QoE With Deep Learning From Multivariate Time-Series

Forecasting Video QoE With Deep Learning From Multivariate Time-Series

Towards Machine Learning Based Analysis of Quality of User Experience (QoUE)

Modelling Quality of Experience for Online Video Advertisement Insertion

Contact Info

Product

Resources

About