scholarly journals A Framework for QoE-Aware 3D Video Streaming Optimisation over Wireless Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Ilias Politis ◽  
Asimakis Lykourgiotis ◽  
Tasos Dagiuklas
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Heterogeneous Wireless Networks ◽  
Video Quality ◽  
Three Dimensional ◽  
Perceptual Quality ◽  
Test Bed ◽  
User Terminal ◽  
The Impact

The delivery of three-dimensional immersive media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics, and user terminal requirements, as well as user’s context. This paper proposes a framework for quality of experience-aware delivering of three-dimensional video across heterogeneous wireless networks. The proposed architecture combines a Media-Aware Proxy (application layer filter), an enhanced version of IEEE 802.21 protocol for monitoring key performance parameters from different entities and multiple layers, and a QoE controller with a machine learning-based decision engine, capable of modelling the perceived video quality. The proposed architecture is fully integrated with the Long Term Evolution Enhanced Packet Core networks. The paper investigates machine learning-based techniques for producing an objective QoE model based on parameters from the physical, the data link, and the network layers. Extensive test-bed experiments and statistical analysis indicate that the proposed framework is capable of modelling accurately the impact of network impairments to the perceptual quality of three-dimensional video user.

Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks

2019 ◽  
pp. 235-254
Author(s):  
Monalisa Ghosh ◽  
Chetna Singhal
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Video Streaming ◽  
Video Transmission ◽  
Video Quality ◽  
Subjective Quality ◽  
Perceptual Quality ◽  
Support Vector ◽  
Machine Learning Model

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.

scholarly journals Distortion-Based Slice Level Prioritization for Real-Time Video over QoS-Enabled Wireless Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Ismail A. Ali ◽  
Martin Fleury ◽  
Mohammed Ghanbari
Keyword(s):  
Wireless Networks ◽  
Quality Of Service ◽  
Real Time ◽  
Wireless Lan ◽  
Video Quality ◽  
Ieee 802.11E ◽  
Video Streams ◽  
The Impact ◽  
Prioritization Scheme

This paper presents a prioritization scheme based on an analysis of the impact on objective video quality when dropping individual slices from coded video streams. It is shown that giving higher-priority classified packets preference in accessing the wireless media results in considerable quality gain (up to 3 dB in tests) over the case when no prioritization is applied. The proposed scheme is demonstrated for an IEEE 802.11e quality-of-service- (QoS-) enabled wireless LAN. Though more complex prioritization systems are possible, the proposed scheme is crafted for mobile interactive or user-to-user video services and is simply implemented within the Main or the Baseline profiles of an H.264 codec.

scholarly journals A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 318
Author(s):  
Merima Kulin ◽  
Tarik Kazaz ◽  
Eli De Poorter ◽  
Ingrid Moerman
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Performance Improvement ◽  
Quality Of Experience ◽  
Data Driven ◽  
Protocol Stack ◽  
Comprehensive Survey ◽  
Network Prediction ◽  
Network Quality

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

Research on the Reliability of Box for Gear Test Bed

2011 ◽  
Vol 121-126 ◽  
pp. 1744-1748
Author(s):  
Xiang Yang Jin ◽  
Tie Feng Zhang ◽  
Li Li Zhao ◽  
He Teng Wang ◽  
Xiang Yi Guan
Keyword(s):  
Power Flow ◽  
Dynamic Performance ◽  
Three Dimensional ◽  
Design Parameters ◽  
The Finite Element Method ◽  
Test Bed ◽  
Kinematics Simulation ◽  
Beveloid Gears ◽  
Overall Performance

To determine the efficiency, load-bearing capacity and fatigue life of beveloid gears with intersecting axes, we design a mechanical gear test bed with closed power flow. To test the quality of its structure and predict its overall performance, we establish a three-dimensional solid model for various components based on the design parameters and adopt the technology of virtual prototyping simulation to conduct kinematics simulation on it. Then observe and verify the interactive kinematic situation of each component. Moreover, the finite element method is also utilized to carry out structural mechanics and dynamics analysis on some key components. The results indicate that the test bed can achieve the desired functionality, and the static and dynamic performance of some key components can also satisfy us.

scholarly journals Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

2021 ◽  
Vol 10 (7) ◽  
pp. 436
Author(s):  
Amerah Alghanim ◽  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Classification Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Semantic Inference ◽  
Road Type ◽  
The Impact

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

scholarly journals The impact of technical parameters on the perception quality of three-dimensional content

2020 ◽  
Vol 29 (4) ◽  
pp. 13-18
Author(s):  
Fabijan Nushi ◽  
◽  
Vladimir Cviljušac ◽  
Lidija Mandić ◽  
◽  
...  
Keyword(s):  
Three Dimensional ◽  
Technical Parameters ◽  
The Impact

A Network Selection Algorithm with Parallel Transmission Based on MADM

2014 ◽  
Vol 556-562 ◽  
pp. 4606-4611 ◽  
Author(s):  
Li Na Zhang ◽  
Qi Zhu
Keyword(s):  
Wireless Networks ◽  
Quality Of Service ◽  
Heterogeneous Wireless Networks ◽  
Network Selection ◽  
Selection Algorithm ◽  
Decision Matrix ◽  
Parallel Transmission ◽  
User Throughput ◽  
Selection Algorithms

At present, most of researches on network selection algorithms are focused on single access. To support seamless mobility and provide better quality of service in heterogeneous wireless networks, this paper proposes a network selection algorithm with parallel transmission based on MADM. In the algorithm, we firstly determine all available wireless networks and consider every subset of these networks as a network scheme. Then we obtain aggregation attributes of every scheme and determine the alternative network schemes. Finally, we build the decision matrix of multiple attributes and determine the optimal scheme by using GRA. Simulation results show that the proposed algorithm can obviously improve user quality of service, improve user throughput, reduce power consumption and price cost per bit.

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