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.

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 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.

scholarly journals VideoCoreCluster: Energy-Efficient, Low-Cost, and Hardware-Assisted Video Transcoding System

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Peng Liu ◽  
Jongwon Yoon ◽  
Ha Ryung Kim ◽  
Suman Banerjee
Keyword(s):  
Video Streaming ◽  
Energy Efficient ◽  
Low Cost ◽  
Scheduling Algorithm ◽  
Video Stream ◽  
Raspberry Pi ◽  
Video Transcoding ◽  
Live Streaming ◽  
Streaming Services

Video streaming is one of the killer applications in recent years. Video transcoding plays an important role in the video streaming service to cope with the various purposes. Specifically, content owners and publishers heavily utilize video transcoders to reconfigure source video in a variety of formats, video qualities, and bitrate to provide end users with the best possible quality of service. In this paper, we present VideoCoreCluster, a low-cost and energy-efficient transcoder cluster that is suitable for live streaming services. We designed and implemented real-time video transcoder cluster using cheap ($35), powerful, and energy-efficient Raspberry Pi. The quality of transcoded video provided by VideoCoreCluster is similar to the best software-based video transcoder while consuming significantly less energy (<3 W). We have proposed a scheduling algorithm based on priority of video stream and transcoding capacity. Our cluster manager provides reliable and scalable streaming services, because it uses the characteristics of adaptive bitrate scheme. We have deployed our transcoding cluster to provide IP-based TV streaming services on our university campus.

scholarly journals Adaptive video transmission over software defined networks

2019 ◽  
Vol 13 (1) ◽  
pp. 152-161
Author(s):  
Fran Wilson Sanabria Navarro ◽  
Juan Gabriel Bustos ◽  
Wilder Eduardo Castellanos Hernández
Keyword(s):  
Video Streaming ◽  
Video Transmission ◽  
Adaptive Transmission ◽  
Software Defined Network ◽  
Software Defined Networks ◽  
Video Streams ◽  
Adaptive Video ◽  
Streaming Services

This paper presents the results of a study on the evaluation of adaptive transmission of video streams using the DASH technique on Software Defined Networks. There are also presented in this document, the description of the tools required for the implementation of the evaluation, as well as a description of the methodology used for the development of the experiments. In addition, the results of an adaptive transmission of a video by using DASH are presented. This transmission was carried out over a software defined network emulated on MININET. The results show that DASH technique easily allows to implement video streaming services that can adapt the quality of the transmission according to the resources available in the network.

scholarly journals Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 621
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Video Streaming ◽  
Network Performance ◽  
Service Providers ◽  
Perceived Quality ◽  
Internet Service Providers ◽  
Performance Parameters ◽  
Internet Service ◽  
Access Points

Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.

scholarly journals No-Reference Depth Map Quality Evaluation Model Based on Depth Map Edge Confidence Measurement in Immersive Video Applications

2019 ◽  
Vol 11 (10) ◽  
pp. 204 ◽  
Author(s):  
Dogan ◽  
Haddad ◽  
Ekmekcioglu ◽  
Kondoz
Keyword(s):  
Quality Evaluation ◽  
Evaluation Model ◽  
Depth Map ◽  
Depth Image ◽  
Depth Maps ◽  
Model Based ◽  
Objective Quality ◽  
Quality Evaluation Model ◽  
Confidence Measurement

When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance.

scholarly journals Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification

2019 ◽  
Vol 9 (11) ◽  
pp. 2297
Author(s):  
Kyeongseon Kim ◽  
Dohyun Kwon ◽  
Joongheon Kim ◽  
Aziz Mohaisen
Keyword(s):  
Video Streaming ◽  
Waiting Time ◽  
Quality Of Experience ◽  
Video Quality ◽  
Live Video ◽  
Streaming Services ◽  
Online Streaming ◽  
Over The Top ◽  
User Content

As the demand for over-the-top and online streaming services exponentially increases, many techniques for Quality of Experience (QoE) provisioning have been studied. Users can take actions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern of users rather than the network condition or video quality. In this context, we propose a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms.

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