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 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.

scholarly journals Scalable Video Streaming for Single-Hop Wireless Networks Using a Contention-Based Access MAC Protocol

2008 ◽  
Vol 2008 ◽  
pp. 1-21
Author(s):  
Monchai Lertsutthiwong ◽  
Thinh Nguyen ◽  
Alan Fern
Keyword(s):  
Wireless Networks ◽  
Video Streaming ◽  
Admission Control ◽  
Packet Loss ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Mac Protocol ◽  
Video Quality ◽  
Streaming Applications

Limited bandwidth and high packet loss rate pose a serious challenge for video streaming applications over wireless networks. Even when packet loss is not present, the bandwidth fluctuation, as a result of an arbitrary number of active flows in an IEEE 802.11 network, can significantly degrade the video quality. This paper aims to enhance the quality of video streaming applications in wireless home networks via a joint optimization of video layer-allocation technique, admission control algorithm, and medium access control (MAC) protocol. Using an Aloha-like MAC protocol, we propose a novel admission control framework, which can be viewed as an optimization problem that maximizes the average quality of admitted videos, given a specified minimum video quality for each flow. We present some hardness results for the optimization problem under various conditions and propose some heuristic algorithms for finding a good solution. In particular, we show that a simple greedy layer-allocation algorithm can perform reasonably well, although it is typically not optimal. Consequently, we present a more expensive heuristic algorithm that guarantees to approximate the optimal solution within a constant factor. Simulation results demonstrate that our proposed framework can improve the video quality up to 26% as compared to those of the existing approaches.

Video Quality Evaluation for H.264 Video Streaming over Wireless Networks

2013 ◽  
Vol 760-762 ◽  
pp. 639-642
Author(s):  
Li Qiang Liu ◽  
Yue Bing Wang ◽  
Quan Feng Yan
Keyword(s):  
Wireless Networks ◽  
Wireless Network ◽  
Video Transmission ◽  
Rapid Development ◽  
Video Quality ◽  
Video Encoding ◽  
Network Bandwidth ◽  
Transmission Quality ◽  
Routing Technology

With the rapid development of computer technology and wireless network communication technology, video encoding technology will be more and more widely applied in the limited resources of the wireless network. Due to the large amount of data of the video transmission , transmission quality of transmit video on the wireless network are varied with different compression parameters, network parameters and network conditions. Simulation results show that the transmission of video over wireless networks, must be based on the current network conditions, choosing the suitable GOP length and quantitative parameters to get the high image quality. In specific applications, network topology, network bandwidth, routing technology and transmission of packet segmentation scheme and other factors will affect the quality of service for video services.

scholarly journals Buffer evaluation model and scheduling strategy for video streaming services in 5G-powered drone using machine learning

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yu Su ◽  
Shuijie Wang ◽  
Qianqian Cheng ◽  
Yuhe Qiu
Keyword(s):  
Machine Learning ◽  
Video Streaming ◽  
Video Transmission ◽  
Evaluation Model ◽  
Video Stream ◽  
Traffic Load ◽  
Model Based ◽  
Streaming Services ◽  
Reward Mechanism

AbstractWith regard to video streaming services under wireless networks, how to improve the quality of experience (QoE) has always been a challenging task. Especially after the arrival of the 5G era, more attention has been paid to analyze the experience quality of video streaming in more complex network scenarios (such as 5G-powered drone video transmission). Insufficient buffer in the video stream transmission process will cause the playback to freeze [1]. In order to cope with this defect, this paper proposes a buffer starvation evaluation model based on deep learning and a video stream scheduling model based on reinforcement learning. This approach uses the method of machine learning to extract the correlation between the buffer starvation probability distribution and the traffic load, thereby obtaining the explicit evaluation results of buffer starvation events and a series of resource allocation strategies that optimize long-term QoE. In order to deal with the noise problem caused by the random environment, the model introduces an internal reward mechanism in the scheduling process, so that the agent can fully explore the environment. Experiments have proved that our framework can effectively evaluate and improve the video service quality of 5G-powered UAV.

scholarly journals Video Transmission In Wireless Multihop Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 3562-3565
Keyword(s):  
Wireless Networks ◽  
Wireless Network ◽  
Video Streaming ◽  
Video Transmission ◽  
Packet Scheduling ◽  
Video Quality ◽  
Video Communications ◽  
Wireless Data ◽  
Wireless Data Transmission ◽  
Wide Range

Multi hop wireless networks are being deployed in many video streaming applications because they have several potential features for next generation wireless communications. Though optimal encoding techniques offers significant quality retention in video transmission still it is insufficient to overcome the challenges ahead over wireless network transmission. In order to support wide range video communications in an efficient way certain Quality of service has to be retained in multi hop wireless network. To address this issue, this paper investigates several encoding and routing protocols video delivery over multi hop wireless networks. Specifically, we first investigate several encoding framework for videos and wireless data transmission over WMNs through individual paths; we then investigate the challenges ahead to formulate resistant routing model for least possible video quality dictions which incorporate channel status as well as the encoder properties over video characteristics. In this framework, routing techniques which can maximally used to achieve good video traffic with improved system performance. However, video streaming also have very stringent delay requirements, which makes it difficult to find optimal routes with the least possible distortions. To address this problem, we investigate several enhanced version of packet scheduling techniques for video communications over multi path multi hob multi user wireless network environment.

scholarly journals A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2851
Author(s):  
Obinna Izima ◽  
Ruairí Fréin ◽  
Ali Malik
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Video Streaming ◽  
Video Quality ◽  
Quality Prediction ◽  
Learning Techniques ◽  
Quality Of Delivery ◽  
Streaming Services ◽  
End To End

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.

A Framework for Evaluation of Video Quality of Service in Wireless Networks

2017 ◽  
pp. 1-29
Author(s):  
Dharm Singh ◽  
Lal Chand Bishnoi
Keyword(s):  
Wireless Networks ◽  
Quality Of Service ◽  
Video Transmission ◽  
Video Quality ◽  
Measuring Technique ◽  
New Techniques ◽  
Ieee 802.11N ◽  
Transmission Quality ◽  
Video Transmission Quality

Today's IEEE 802.11n WLAN are capable of delivering the essential bandwidth for video by using MPEG-4 or H.264 codec compression. However, 802.11n devices are popular for delivering wireless video transmission. Even though there are some challenges for these devices specifically severe network congestion that degrade the quality of video transmission. For assessing the video transmission quality during a simulation on 802.11n WLAN technology, we extended a framework and toolset (802.11nMyEvalVid) which can be used to measures the QoS constraints in wireless networks, such as throughput, delays, and end-to-end delay. However, it also supports PSNR, which is a received video quality measuring technique based on a comparison of frame-by-frame. This chapter focused on a framework 802.11nMyEvalVid that can be used for research and evaluating new techniques for MAC-layer optimizations for simulating MPEG-4, H.264/AVC and H.264/SVC video over 802.11n WLAN in a more efficient and reliable way.

A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides

2019 ◽  
Vol 15 (3) ◽  
pp. 206-211 ◽  
Author(s):  
Jihui Tang ◽  
Jie Ning ◽  
Xiaoyan Liu ◽  
Baoming Wu ◽  
Rongfeng Hu
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Position ◽  
Cell Penetrating Peptides ◽  
Support Vector ◽  
Cell Penetration ◽  
Drug Candidates ◽  
Machine Learning Model ◽  
Cell Penetrating ◽  
Novel Method

<P>Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. </P><P> Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. </P><P> Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. </P><P> Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.</P>

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