Quality of Experience for High Definition Presentations - Case: Digital Cinema

The current paper focuses on validating an implementation of a state-of-the art audiovisual (AV) technologies setup for live broadcasting of cultural shows, via broadband Internet. The main objective of the work was to study, configure, and setup dedicated audio-video equipment for the processes of capturing, processing, and transmission of extended resolution and high fidelity AV content in order to increase realism and achieve maximum audience sensation. Internet2 and GEANT broadband telecommunication networks were selected as the most applicable technology to deliver such traffic workloads. Validation procedures were conducted in combination with metric-based quality of service (QoS) and quality of experience (QoE) evaluation experiments for the quantification and the perceptual interpretation of the quality achieved during content reproduction. The implemented system was successfully applied in real-world applications, such as the transmission of cultural events from Thessaloniki Concert Hall throughout Greece as well as the reproduction of Philadelphia Orchestra performances (USA) via Internet2 and GEANT backbones.

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Assessing quality of experience for high definition video streaming under diverse packet loss patterns

Signal Processing Image Communication ◽

10.1016/j.image.2012.09.006 ◽

2013 ◽

Vol 28 (8) ◽

pp. 903-916 ◽

Cited By ~ 5

Author(s):

Mikołaj Leszczuk ◽

Lucjan Janowski ◽

Piotr Romaniak ◽

Zdzisław Papir

Keyword(s):

Video Streaming ◽

Packet Loss ◽

Quality Of Experience ◽

High Definition ◽

High Definition Video

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Quality of Experience Assessment of Video Quality in Social Clouds

Wireless Communications and Mobile Computing ◽

10.1155/2017/8313942 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 8

Author(s):

Asif Ali Laghari ◽

Hui He ◽

Shahid Karim ◽

Himat Ali Shah ◽

Nabin Kumar Karn

Keyword(s):

Video Compression ◽

Quality Of Experience ◽

Service Providers ◽

Video Quality ◽

Original Video ◽

File Size ◽

High Definition ◽

High Quality ◽

Video Sharing

Video sharing on social clouds is popular among the users around the world. High-Definition (HD) videos have big file size so the storing in cloud storage and streaming of videos with high quality from cloud to the client are a big problem for service providers. Social clouds compress the videos to save storage and stream over slow networks to provide quality of service (QoS). Compression of video decreases the quality compared to original video and parameters are changed during the online play as well as after download. Degradation of video quality due to compression decreases the quality of experience (QoE) level of end users. To assess the QoE of video compression, we conducted subjective (QoE) experiments by uploading, sharing, and playing videos from social clouds. Three popular social clouds, Facebook, Tumblr, and Twitter, were selected to upload and play videos online for users. The QoE was recorded by using questionnaire given to users to provide their experience about the video quality they perceive. Results show that Facebook and Twitter compressed HD videos more as compared to other clouds. However, Facebook gives a better quality of compressed videos compared to Twitter. Therefore, users assigned low ratings for Twitter for online video quality compared to Tumblr that provided high-quality online play of videos with less compression.

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Cloud Computing: Secure Transmission of High Definition Videos Using Cryptographic Approach

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.19.15057 ◽

2018 ◽

Vol 7 (2.19) ◽

pp. 94

Author(s):

Anwar Basha.H ◽

S Amrit Sai ◽

Sanjnah A ◽

Srimathi M

Keyword(s):

Cloud Computing ◽

Quality Of Experience ◽

Sensitive Information ◽

Data Confidentiality ◽

High Definition ◽

Configurable System ◽

Attribute Based Access Control ◽

The Time Domain ◽

Secure Transmission

Cloud computing facilitates ubiquitous access to shared pool of configurable system resources and services over the Internet. Often due to shared access to this massive amount of data there is equal chances of risk. Transferring sensitive information in the form of text, audio or video, over the cloud one cannot guarantee the safety of the file. This paper assesses the effect of transmission of High Definition videos with the help of cloud-based servers that will improve the security of data being transmitted as well as enhance the quality of experience for end-users. We propose to share video contents to a selective group of people using the Time Domain Attribute based Access Control(TAAC) schema and generate keys using cryptographic method which gives the much needed protection to access these videos. Further stegnographic approaches are practised to maintain the data confidentiality.

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UBAR

ACM Transactions on Embedded Computing Systems ◽

10.1145/3441644 ◽

2021 ◽

Vol 20 (3) ◽

pp. 1-25

Author(s):

Elham Shamsa ◽

Alma Pröbstl ◽

Nima TaheriNejad ◽

Anil Kanduri ◽

Samarjit Chakraborty ◽

...

Keyword(s):

Resource Management ◽

Quality Of Experience ◽

State Of The Art ◽

State Of Charge ◽

User Preference ◽

Management Approach ◽

High Quality ◽

Trade Off ◽

Run Time

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-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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