scholarly journals Moving QoE for monitoring DASH video streaming: models and a study of multiple mobile clients

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sheyda Kiani Mehr ◽  
Prasad Jogalekar ◽  
Deep Medhi
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Service Providers ◽  
Switching Frequency ◽  
Mobility Patterns ◽  
Mobile Client ◽  
Ensemble Performance ◽  
Objective Quality ◽  
Multiple Clients

AbstractObjective Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) video streaming has received considerable attention in recent years. While there are a number of objective QoE models, a limitation of the current models is that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. For content service providers, QoE observed is important to monitor to understand ensemble performance during streaming such as for live events or concurrent streaming when multiple clients are streaming. For this purpose, we propose Moving QoE (MQoE, in short) models to measure QoE during periodically during video streaming for multiple simultaneous clients. Our first model MQoE_RF is a nonlinear model considering the bitrate gain and sensitivity from bitrate switching frequency. Our second model MQoE_SD is a linear model that focuses on capturing the standard deviation in the bitrate switching magnitude among segments along with the bitrate gain. We then study the effectiveness of both models in a multi-user mobile client environment, with the mobility patterns being based on traces from a train, a car, or a ferry. We implemented the study on the GENI testbed. Our study shows that our MQoE models are more accurate in capturing the QoE behavior during transmission than static QoE models. Furthermore, our MQoE_RF model captures the sensitivity due to bitrate switching frequency more effectively while MQoE_SD captures the sensitivity due to the magnitude of the bitrate switching. Either models are suitable for content service providers for monitoring video streaming based on their preference.

Machine Learning based KPI Monitoring of Video Streaming Traffic for QoE Estimation

2021 ◽  
Vol 48 (4) ◽  
pp. 33-36
Author(s):  
Özge Celenk ◽  
Thomas Bauschert ◽  
Marcus Eckert
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Video Streaming ◽  
Quality Of Experience ◽  
Service Providers ◽  
Early Stage ◽  
Traffic Information ◽  
Streaming Traffic ◽  
Testing Accuracy

Quality of Experience (QoE) monitoring of video streaming traffic is crucial task for service providers. Nowadays it is challenging due to the increased usage of end-to-end encryption. In order to overcome this issue, machine learning (ML) approaches for QoE monitoring have gained popularity in the recent years. This work proposes a framework which includes a machine learning pipeline that can be used for detecting key QoE related events such as buffering events and video resolution changes for ongoing YouTube video streaming sessions in real-time. For this purpose, a ML model has been trained using YouTube streaming traffic collected from Android devices. Later on, the trained ML model is deployed in the framework's pipeline to make online predictions. The ML model uses statistical traffic information observed from the network-layer for learning and predicting the video QoE related events. It reaches 88% overall testing accuracy for predicting the video events. Although our work is yet at an early stage, the application of the ML model for online detection and prediction of video events yields quite promising results.

scholarly journals Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 621
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Video Streaming ◽  
Network Performance ◽  
Service Providers ◽  
Perceived Quality ◽  
Internet Service Providers ◽  
Performance Parameters ◽  
Internet Service ◽  
Access Points

Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.

Quality of Experience evaluation of 4k Video Streaming using Referenced Metrics

2021 ◽  
Author(s):  
Edma V.C. Urtiga Mattos ◽  
Gustavo M. Torres ◽  
Mateus O. Da Silva ◽  
Victor L.G. Calvacante ◽  
Adriel V. Dos Santos ◽  
...  
Keyword(s):  
Video Streaming ◽  
Quality Of Experience

Quality of Experience (QoE) for Wireless Video Over Critical Communication Systems

2017 ◽  
pp. 132-167
Author(s):  
Emad Danish ◽  
Mazin I. Alshamrani
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Communication Systems ◽  
Quality Of Experience ◽  
Service Providers ◽  
Wireless Video ◽  
User Centric ◽  
Near Future

Video streaming is expected to acquire a massive share of the global internet traffic in the near future. Meanwhile, it is expected that most of the global traffic will be carried over wireless networks. This trend translates into considerable challenges for Service Providers (SP) in terms of maintaining consumers' Quality of Experience (QoE), energy consumption, utilisation of wireless resources, and profitability. However, the majority of Radio Resource Allocation (RRA) algorithms only consider enhancing Quality of Service (QoS) and network parameters. Since this approach may end up with unsatisfied customers in the future, it is essential to develop innovative RRA algorithms that adopt a user-centric approach based on users' QoE. This chapter focus on wireless video over Critical communication systems that are inspired by QoE perceived by end users. This chapter presents a background to introduce the reader to this area, followed by a review of the related up-to-date literature.

Quality of Internet Service as Experienced by Tanzanians in 2016

2017 ◽  
Vol 9 (4) ◽  
pp. 12-31
Author(s):  
Eliamani Sedoyeka
Keyword(s):  
Social Media ◽  
Quality Of Experience ◽  
Service Providers ◽  
Qualitative Approach ◽  
Internet Service ◽  
Policy Makers ◽  
Internet Users ◽  
Telecommunication Companies ◽  
Article Quality

In this article, Quality of Experience (QoE) is discussed as experienced by Tanzanian internet users for the second biannual of 2016. It presents findings of the research that aimed at among other things, finding out the QoE in internet services offered by telecommunication companies and other internet service providers in the country. A qualitative approach was used to establish practical quality of experience issues considered important by Tanzanians. Online questionnaires distributed over social media mainly WhatsApp and Facebook were used to ask users about their experiences of the services they had been receiving, in which over 2000 responses were collected from all districts of Tanzania. It was established that usability, quality of service, price and after sale support were the main issues found to influence quality of experience for many. The findings in this article are useful for academicians, QoS and QoE researchers, policy makers and ICT professionals.

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