Concealment Support and Error Resilience for HEVC to Improve Consumer Quality of Experience

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

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Quality-Of-Experience Evaluation of 8K Ultra-High-Definition Television

10.1109/icip42928.2021.9506605 ◽

2021 ◽

Author(s):

Yoshiaki Shishikui

Keyword(s):

Quality Of Experience ◽

High Definition ◽

High Definition Television

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Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

IEEE Access ◽

10.1109/access.2021.3091844 ◽

2021 ◽

pp. 1-1

Author(s):

Sajeeb Saha ◽

Md. Ahsan Habib ◽

Tamal Adhikary ◽

Md. Abdur Razzaque ◽

Md. Mustafizur Rahman ◽

...

Keyword(s):

Mobile Device ◽

Quality Of Experience ◽

Incentive Mechanism ◽

Mobile Device Cloud

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What's my App?

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3466826.3466841 ◽

2021 ◽

Vol 48 (4) ◽

pp. 41-44

Author(s):

Dena Markudova ◽

Martino Trevisan ◽

Paolo Garza ◽

Michela Meo ◽

Maurizio M. Munafo ◽

...

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Real Time ◽

Language Processing ◽

Traffic Management ◽

Quality Of Experience ◽

Broadband Internet ◽

Management Policies ◽

Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

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Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3466826.3466840 ◽

2021 ◽

Vol 48 (4) ◽

pp. 37-40

Author(s):

Nikolas Wehner ◽

Michael Seufert ◽

Joshua Schuler ◽

Sarah Wassermann ◽

Pedro Casas ◽

...

Keyword(s):

Neural Networks ◽

Time Series ◽

Recurrent Neural Networks ◽

Quality Of Experience ◽

Unique Feature ◽

Time Series Modeling ◽

Web Browsing ◽

Large Dataset ◽

Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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