scholarly journals Guided MAC to Enhance Quality of Experience of Video Data Transmission over Massive MIMO

2021 ◽  
Vol 1947 (1) ◽  
pp. 012036
Author(s):  
Nitish Bhardwaj ◽  
Diwakar Bhardwaj
Keyword(s):  
Data Transmission ◽  
Massive Mimo ◽  
Quality Of Experience ◽  
Video Data

scholarly journals Guided MAC to Enhance Quality of Experience of Video Data Transmission over Massive MIMO

2021 ◽  
Author(s):  
Diwakar Bhardwaj ◽  
Keyword(s):  
Massive Mimo ◽  
High Speed ◽  
Quality Of Experience ◽  
Mimo System ◽  
Video Data ◽  
Mac Layer ◽  
Mimo Channels ◽  
Noisy Channels ◽  
Mimo Technology

Massive MIMO (M-MIMO) system comprises of multiple number of antennas to achieve energy- efficiency and large gains in spectral-efficiency in comparison to existing MIMO technology. High speed and Quality of Experience (QoE) of video data over wireless communication has always been a challenge for the researchers due to scarcity of the bandwidth, fading and interference. The channels with high noise corrupt the transmitted video and results in poor QoE of at the receiver. Therefore, to maintain the quality of transmitted video, it is highly desirable to identify noisy channels and avoid transmission over them. This paper deals with QoE of the transmitted video over Massive MIMO channels. The channels are categorized into two categories: good and bad depending upon the value of Signal to Interference and Noise Ratio (SINR). A channel above the minimum acceptable value (threshold) of SINR is categorized as good channel otherwise bad channel. A Guided MAC layer (GMAC) protocol is designed to transmit the video data over good channels only and to discard the transmission over bad channels.

scholarly journals QoS Analysis of Video Streaming in the UAV Networks with WiFi Standards

2021 ◽  
Vol 24 (4) ◽  
pp. 73-79
Author(s):  
Kirill Eduardovich Korepanov ◽  
Irina Alekseevna Kaisina ◽  
Roman Eduardovich Shibanov ◽  
Albert Vinerovich Abilov ◽  
Mohammed Amin Lamri
Keyword(s):  
Data Transmission ◽  
Transmission Rate ◽  
Video Stream ◽  
Video Data ◽  
Network Simulator ◽  
Coding Schemes ◽  
Ground Station ◽  
Transmission Parameters ◽  
Aerial Vehicle

The paper presents the results of simulation of the process of video data transmission from an unmanned aerial vehicle (UAV) to a ground station using the IEEE 802.11 family standards (802.11n, 802.11ac and 802.11ax), with the ability to change modulation indices, coding schemes and data transfer rate in a network simulator NS-3. The aim of the work is to analyze the characteristics of the quality of video data transmission in the UAV network for various Wi-Fi standards, which allows determining the most suitable standard for the transmission of video data in the UAV network, depending on the distances between nodes and the required frequency band. A scenario is considered in which an unmanned aerial vehicle (UAV) hovering in the air was transmitting a video stream to a ground station, while the distance between nodes increased, and the transmission rate was maintained at the same level close to the transmission rate of the real video stream. The simulation was carried out in several stages for a more detailed study of the dependence of the packet loss of the transmitted data on the change in modulation indices, coding schemes and other parameters. Based on the simulation results, the characteristics of the video data transmission quality were obtained as a relation between the Packet delivery rate (PDR) and distance between nodes for different transmission parameters for each considered standard of the IEEE 802.11 family. Based on the results obtained, conclusions were drawn about the influence of transmission parameters on the quality of service characteristics. The study was carried out in an open-source network simulator NS-3, which implements build-in libraries that are necessary for high-quality simulation of data streaming transmission and allows you to set a wide range of parameters to obtain realistic results. The results of the work may be of interest to UAV manufacturers when planning missions in which the choice of Wi-Fi standard used as a channel for transmitting video data is crucial.

Adaptive Transmission Control Based on QoE Measurement of Invalid Data Rate

2014 ◽  
Vol 513-517 ◽  
pp. 2063-2067
Author(s):  
Lin Guang Lai ◽  
Lei Xu ◽  
Chang Sheng Cao
Keyword(s):  
Packet Loss ◽  
Data Transmission ◽  
Loss Rate ◽  
Code Rate ◽  
Adaptive Transmission ◽  
Data Rate ◽  
Video Data ◽  
Transmission Control ◽  
Rate Experiment

In the real-time network video surveillance system which uses UDP for data transmission, the packet loss and delay of H.264 video data unit would cause image pause and blur. The paused and blurry image affects the quality of users’ experience. In this paper, we put forward Idr (invalid data rate) to integrate the delay and packet loss rate and chooses code rate as the target of adjustment. We propose an adaptive transmission approach based on QoE Measurement of invalid data rate. Experiment result shows that, this approach can improve the video fluency and clarity as well as user experience.

scholarly journals TeleSDC

2021 ◽  
Vol 7 (2) ◽  
pp. 403-406
Author(s):  
Tobias Pabst ◽  
Dominik Stegemann ◽  
Christoph Georgi ◽  
Martin Kasparick ◽  
Julian Suleder ◽  
...  
Keyword(s):  
Medical Device ◽  
Data Transmission ◽  
Radio Networks ◽  
Patient Data ◽  
Video Data ◽  
Open Communication ◽  
Emergency Personnel ◽  
Public Networks ◽  
Additional Value

Abstract Telemedicine promises to increase the quality of emergency treatments. Besides the transfer of speech and video data, medical device and patient data will add additional value to the tele-guided emergency personnel. In this paper, we develop a concept for transmitting device and patient data via the open communication standards SDC in a telemedical context, including data transmission over mobile radio networks while considering the limitations of public networks, and opening new usage scenarios for telemedicine.

scholarly journals A Study on Task Scheduling in Mobile Cloud Computing

2018 ◽  
pp. 244-246
Author(s):  
J. Arockia Mary ◽  
P. Xavier Jeba ◽  
P. Mercy
Keyword(s):  
Cloud Computing ◽  
Mobile Device ◽  
Data Transmission ◽  
Mobile Applications ◽  
Quality Of Experience ◽  
Mobile Cloud Computing ◽  
Limited Resource ◽  
Mobile Cloud ◽  
Task Dependency

In mobile device, the resources such as computation, storage, power are limited. Quality of Experience (QoE) of user in these limited resource mobile device is not satisfied. Mobile cloud computing is a new computation paradigm to increase Quality of Service (QoS) of mobile applications by scheduling the offloaded tasks into the cloud. The scheduling of tasks is done in four architectures of mobile cloud computing. Two types of scheduling are done with lot of constraints such as data transmission, task dependency and cost etc. Different scheduling techniques are developed to improve the QoE of mobile users.

UBAR

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.

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

IEEE Access ◽  
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

What's my App?

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.

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

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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