A quantitative relationship between Application Performance Metrics and Quality of Experience for Over-The-Top video

2018 ◽  
Vol 142 ◽  
pp. 194-207 ◽  
Author(s):  
Weiwei Li ◽  
Petros Spachos ◽  
Mark Chignell ◽  
Alberto Leon-Garcia ◽  
Leon Zucherman ◽  
...  
Keyword(s):  
Quality Of Experience ◽  
Performance Metrics ◽  
Quantitative Relationship ◽  
Application Performance ◽  
Over The Top

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.

scholarly journals Crowdsourcing Framework for QoE-Aware SD-WAN

2021 ◽  
Vol 13 (8) ◽  
pp. 209
Author(s):  
Ibtihal Ellawindy ◽  
Shahram Shah Heydari
Keyword(s):  
Service Quality ◽  
Real Time ◽  
Quality Of Experience ◽  
Network Resources ◽  
Application Performance ◽  
Network Parameters ◽  
Subjective Assessments ◽  
Interactive Nature ◽  
Direct Feedback

Quality of experience (QoE) is an important measure of users’ satisfaction regarding their network-based services, and it is widely employed today to provide a real assessment of the service quality as perceived by the end users. QoE measures can be used to improve application performance, as well as to optimize network resources and reallocate them as needed when the service quality degrades. While quantitative QoE assessments based on network parameters may provide insights into users’ experience, subjective assessments through direct feedback from the users have also gathered interest recently due to their accuracy and interactive nature. In this paper, we propose a framework that can be used to collect real-time QoE feedback through crowdsourcing and forward it to network controllers to enhance streaming routes. We analyze how QoE can be affected by different network conditions, and how different streaming protocols compare against each other when the network parameters change dynamically. We also compare the real-time user feedback to predefined network changes to measure if participants will be able to identify all degradation events, as well as to examine which combination of degradation events are noticeable to the participants. Our aim is to demonstrate that real-time QoE feedback can enhance cloud-based services and can adjust service quality on the basis of real-time, active participants’ interactions.

Context-Aware Content Delivery

2015 ◽  
pp. 474-499
Author(s):  
Hassnaa Moustafa ◽  
V. Srinivasa Somayazulu ◽  
Yiting Liao
Keyword(s):  
Quality Of Experience ◽  
Service Providers ◽  
Content Delivery ◽  
Context Aware ◽  
Video Delivery ◽  
Network Resources ◽  
Personalized Services ◽  
Over The Top ◽  
Required Quality

The huge changes in multimedia and video consumption styles are leading to different challenges for the current Internet architecture in order to support the required quality of experience. A comprehensive solution to these would help the service providers and over-the-top players (OTT) to differentiate their services and the network operators to handle ever growing demands on network resources in an era of slower growth in revenues. This chapter discusses the requirements for and approaches to enhanced content delivery architectures, video delivery standards and current and future content transport mechanisms. The chapter also discusses the Quality of Experience (QoE) metrics and management for video content and introduces context-awareness in the video delivery chain. It also provides several examples for context-aware content delivery and personalized services.

Acceptability and Quality of Experience in over the top video

2015 ◽  
Author(s):  
Petros Spachos ◽  
Weiwei Li ◽  
Mark Chignell ◽  
Alberto Leon-Garcia ◽  
Leon Zucherman ◽  
...  
Keyword(s):  
Quality Of Experience ◽  
Over The Top

Correlating Quality of Experience and Quality of Service for Network Applications

2010 ◽  
pp. 326-351 ◽  
Author(s):  
Mihai Ivanovici ◽  
Razvan Beuran
Keyword(s):  
Quality Of Service ◽  
Video Transmission ◽  
Quality Of Experience ◽  
Point Of View ◽  
File Transfer ◽  
Application Performance ◽  
Network Applications ◽  
Network Application ◽  
The One

There is a significant difference between what a network application experiences as quality at network level, and what the user perceives as quality at application level. From the network point of view, applications require certain delay, bandwidth and packet loss bounds to be met – ideally zero delay and zero loss. However, users should not be directly concerned with network conditions, and furthermore they are usually neither able to measure nor predict them. Users only expect good application performance, i.e., a fast and reliable file transfer, high quality for voice or video transmission, and so on, depending on the application being used. This is true both in wired as well as wireless networks. In order to understand network application behavior, as well as the interaction between the application and the network, one must perform a delicate task – the one of correlating the Quality of Service (QoS), i.e., the degradation induced at network level (as a measure of what the application experiences), with the Quality of Experience (QoE), i.e., the degradation perceived by the user at application level (as a measure of the user-perceived quality) (Ivanovici, 2006). This is done by simultaneously measuring the QoS degradation and the application QoE on an end-to-end basis. These measures must be then correlated by taking into account their temporal relationship. Assessing the correlation between QoE and QoS makes it possible to predict application performance given a known QoS degradation level, and to determine the QoS bounds that are required in order to attain a desired QoE level.

scholarly journals Quality Enhanced Multimedia Content Delivery for Mobile Cloud with Deep Reinforcement Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Muhammad Saleem ◽  
Yasir Saleem ◽  
H. M. Shahzad Asif ◽  
M. Saleem Mian
Keyword(s):  
Quality Of Experience ◽  
Performance Metrics ◽  
Optimal Solution ◽  
Content Delivery ◽  
Multimedia Streaming ◽  
Multimedia Content ◽  
Quality Level ◽  
Limited Bandwidth ◽  
Multimedia Content Delivery

The importance of multimedia streaming using mobile devices has increased considerably. The dynamic adaptive streaming over HTTP is an efficient scheme for bitrate adaptation in which video is segmented and stored in different quality levels. The multimedia streaming with limited bandwidth and varying network environment for mobile users affects the user quality of experience. We have proposed an adaptive rate control using enhanced Double Deep Q-Learning approach to improve multimedia content delivery by switching quality level according to the network, device, and environment conditions. The proposed algorithm is thoroughly evaluated against state-of-the-art heuristic and learning-based algorithms. The performance metrics such as PSNR, SSIM, quality of experience, rebuffering frequency, and quality variations are evaluated. The results are obtained using real network traces which shows that the proposed algorithm outperforms the other schemes in all considered quality metrics. The proposed algorithm provides faster convergence to the optimal solution as compared to other algorithms considered in our work.

scholarly journals Traffic Steering for eMBB in Multi-Connectivity Scenarios

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2063
Author(s):  
Jesús Burgueño ◽  
Isabel de-la-Bandera ◽  
David Palacios ◽  
Raquel Barco
Keyword(s):  
Quality Of Experience ◽  
Performance Metrics ◽  
5G Networks ◽  
Network Nodes ◽  
Opinion Score ◽  
Data Rates ◽  
Throughput Improvement ◽  
Radio Resources ◽  
Traffic Steering

Multi-connectivity (MC) is one of the most important features to be introduced in 5G networks, allowing User Equipment (UE) to simultaneously aggregate radio resources from several network nodes to enhance both data rates and reliability. Thus, this feature enables a further flexibility in the allocation of resources to the UEs in order to fulfil the users’ requirements in more complex 5G scenarios. This paper takes advantage of this wide flexibility to present a traffic steering approach that determines the amount of traffic to be held by each of the serving nodes in a multi-connectivity scenario. In this sense, the proposed technique is based on network and UE performance metrics in order to maximize the users’ perceived quality of experience (QoE) for enhanced Mobile Broadband (eMBB) services. It is then compared with a homogeneous traffic split among the serving nodes, with a single-connectivity approach and with state-of-the-art solutions. The benefits are analysed in terms of throughput and Mean Opinion Score (MOS), which is the main QoE metric. The analysis shows that a noticeable UE throughput improvement is reached when the proposed traffic steering method is applied. Consequently, this enhancement is noticed in the users’ QoE, which can lead to a reduction of operating expenses (OPEX) of the network.

scholarly journals A generic quantitative relationship between quality of experience and quality of service

IEEE Network ◽  
2010 ◽  
Vol 24 (2) ◽  
pp. 36-41 ◽  
Author(s):  
Markus Fiedler ◽  
Tobias Hossfeld ◽  
Phuoc Tran-Gia
Keyword(s):  
Quality Of Service ◽  
Quality Of Experience ◽  
Quantitative Relationship
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