scholarly journals Optimization of Quality of Experience for Video Traffic

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Transmission Rate ◽  
Video On Demand ◽  
Video Traffic ◽  
Wired Networks ◽  
Internet Users ◽  
Strong Focus ◽  
Adaptive Architectures ◽  
Real Time Information

<div>The rapid shift toward video on-demand and real time information systems has affected mobile as well as wired networks. The research community has placed a strong focus on optimizing the Quality of Experience (QoE) of video traffic, mainly because video is popular among Internet users. Techniques have been proposed in different directions towards improvement of the perception of video users. This paper investigates the performance of a novel cross-layer architecture for optimizing the QoE of video traffic. The proposed architecture is compared to two other architectures; non-adaptive and adaptive. For the former, video traffic is sent without adaptation, whereas for the later video sources adapt their transmission rate. Both are compared in terms of the mean opinion score of video sessions, number of sessions, delay, packet drop ratio, jitter and utilization. The results from extensive simulations show that the proposed architecture outperforms the non-adaptive and adaptive architectures for video traffic.</div>

scholarly journals Optimization of Quality of Experience for Video Traffic

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Transmission Rate ◽  
Video On Demand ◽  
Video Traffic ◽  
Wired Networks ◽  
Internet Users ◽  
Strong Focus ◽  
Adaptive Architectures ◽  
Real Time Information

<div>The rapid shift toward video on-demand and real time information systems has affected mobile as well as wired networks. The research community has placed a strong focus on optimizing the Quality of Experience (QoE) of video traffic, mainly because video is popular among Internet users. Techniques have been proposed in different directions towards improvement of the perception of video users. This paper investigates the performance of a novel cross-layer architecture for optimizing the QoE of video traffic. The proposed architecture is compared to two other architectures; non-adaptive and adaptive. For the former, video traffic is sent without adaptation, whereas for the later video sources adapt their transmission rate. Both are compared in terms of the mean opinion score of video sessions, number of sessions, delay, packet drop ratio, jitter and utilization. The results from extensive simulations show that the proposed architecture outperforms the non-adaptive and adaptive architectures for video traffic.</div>

scholarly journals Improving Perceived Quality of Live Adaptative Video Streaming

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 948
Author(s):  
Carlos Eduardo Maffini Santos ◽  
Carlos Alexandre Gouvea da Silva ◽  
Carlos Marcelo Pedroso
Keyword(s):  
Video Streaming ◽  
Short Term Memory ◽  
Transmission Rate ◽  
Video Quality ◽  
Video On Demand ◽  
Live Streaming ◽  
Network Congestion ◽  
Congested Networks ◽  
Over The Top

Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.

Quality of Experience-based Routing of Video Traffic for Overlay and ISP Networks

2018 ◽  
Author(s):  
Giacomo Calvigioni ◽  
Ramon Aparicio-Pardo ◽  
Lucile Sassatelli ◽  
Jeremie Leguay ◽  
Paolo Medagliani ◽  
...  
Keyword(s):  
Quality Of Experience ◽  
Video Traffic

Quality of Internet Service as Experienced by Tanzanians in 2016

2017 ◽  
Vol 9 (4) ◽  
pp. 12-31
Author(s):  
Eliamani Sedoyeka
Keyword(s):  
Social Media ◽  
Quality Of Experience ◽  
Service Providers ◽  
Qualitative Approach ◽  
Internet Service ◽  
Policy Makers ◽  
Internet Users ◽  
Telecommunication Companies ◽  
Article Quality

In this article, Quality of Experience (QoE) is discussed as experienced by Tanzanian internet users for the second biannual of 2016. It presents findings of the research that aimed at among other things, finding out the QoE in internet services offered by telecommunication companies and other internet service providers in the country. A qualitative approach was used to establish practical quality of experience issues considered important by Tanzanians. Online questionnaires distributed over social media mainly WhatsApp and Facebook were used to ask users about their experiences of the services they had been receiving, in which over 2000 responses were collected from all districts of Tanzania. It was established that usability, quality of service, price and after sale support were the main issues found to influence quality of experience for many. The findings in this article are useful for academicians, QoS and QoE researchers, policy makers and ICT professionals.

scholarly journals A Mobile Fog Computing-Assisted DASH QoE Prediction Scheme

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hongyun Zheng ◽  
Yongxiang Zhao ◽  
Xi Lu ◽  
Rongzhen Cao
Keyword(s):  
Quality Of Experience ◽  
Fog Computing ◽  
Video Quality ◽  
Adaptive Streaming ◽  
Network Operator ◽  
Video Traffic ◽  
Client Server ◽  
Prediction Scheme ◽  
Network Equipment

Video service has become a killer application for mobile terminals. For providing such services, most of the traffic is carried by the Dynamic Adaptive Streaming over HTTP (DASH) technique. The key to improve video quality perceived by users, i.e., Quality of Experience (QoE), is to effectively characterize it by using measured data. There have been many literatures that studied this issue. Some existing solutions use probe mechanism at client/server, which, however, are not applicable to network operator. Some other solutions, which aimed to predict QoE by deep packet parsing, cannot work properly as more and more video traffic is encrypted. In this paper, we propose a fog-assisted real-time QoE prediction scheme, which can predict the QoE of DASH-supported video streaming using fog nodes. Neither client/server participations nor deep packet parsing at network equipment is needed, which makes this scheme easy to deploy. Experimental results show that this scheme can accurately detect QoE with high accuracy even when the video traffic is encrypted.

scholarly journals Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications

2020 ◽  
Vol 10 (10) ◽  
pp. 3662 ◽  
Author(s):  
Abdul Wahab ◽  
Nafi Ahmad ◽  
John Schormans
Keyword(s):  
Error Propagation ◽  
Quality Of Experience ◽  
Statistical Error ◽  
Video On Demand ◽  
Sampling Errors ◽  
On Demand ◽  
Subjective Testing ◽  
Qos Parameters ◽  
Latency Jitter

In addition to the traditional Quality of Service (QoS) metrics of latency, jitter and Packet Loss Ratio (PLR), Quality of Experience (QoE) is now widely accepted as a numerical proxy for the actual user experience. The literature has reported many mathematical mappings between QoE and QoS, where the QoS parameters are measured by the network providers using sampling. Previous research has focussed on sampling errors in QoS measurements. However, the propagation of these sampling errors in QoS through to the QoE values has not been evaluated before. This is important: without knowing how sampling errors propagate through to QoE estimates there is no understanding of the precision of the estimates of QoE, only of the average QoE value. In this paper, we used industrially acquired measurements of PLR and jitter to evaluate the sampling errors. Additionally, we evaluated the correlation between these QoS measurements, as this correlation affects errors propagating to the estimated QoE. Focusing on Video-on-Demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLR and jitter. The resulting mathematical functions, and the theory of error propagation, were used to evaluate the error propagated to QoE. This error in estimated QoE was represented as confidence interval width. Using the guidelines of UK government for sampling in a busy hour, our results indicate that confidence intervals around estimated the Mean Opinion Score (MOS) rating of QoE can be between MOS = 1 to MOS = 4 at targeted operating points of the QoS parameters. These results are a new perspective on QoE evaluation and are of potentially great significance to all organisations that need to estimate the QoE of VoD applications precisely.

Measurement of Quality of Experience of Video-on-Demand Services: A Survey

2016 ◽  
Vol 18 (1) ◽  
pp. 401-418 ◽  
Author(s):  
Parikshit Juluri ◽  
Venkatesh Tamarapalli ◽  
Deep Medhi
Keyword(s):  
Quality Of Experience ◽  
Video On Demand ◽  
On Demand

scholarly journals Mechanisms for QoE optimisation of Video Traffic: A review paper

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Review Paper ◽  
End Users ◽  
The Internet ◽  
Video Traffic ◽  
Future Directions ◽  
The Past ◽  
New Methods ◽  
Increasing Demand

Transmission of video traffic over the Internet has grown exponentially in the past few years<br>with no sign of waning. This increasing demand for video services has changed user expectation of quality. Various mechanisms have been proposed to optimise the Quality of Experience (QoE) of end users’ video. Studying these approaches are necessary for new methods to be proposed or combination of existing ones to be tailored. We discuss challenges facing the optimisation of QoE for video traffic in this paper. It surveys and classifies these mechanisms based on their functions. The limitation of each of them is identified and future directions are highlighted.

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