Mohammed Alreshoodi

An empirical study based on a fuzzy logic system to assess the QoS/QoE correlation for layered video streaming

A model that can predict an end user satisfaction or QoE (Quality of Experience) directly from the network QoS (Quality of Service) is still illusive in the field of image processing and is completely absent in multi-layered video. This motivates the derivation of a meaningful QoS to QoE mapping function to allow one to be predicted in the absence of the other. This paper presents an affine fuzzy logic based system that can map the QoS to QoE and can be extended to layered video streaming. The proposed methodology employs a learning system which optimizes the coded layered video for best QoE. Four QoS parameters are chosen as the inputs of the designed model, while the output is the Peak Signal-to-Noise Ratio (PSNR). The designed membership functions and the fuzzy rules extracted from the input and the output enable the proposed model to identify and learn the video QoE.

QoE Prediction Model Based on Fuzzy Logic System for Different Video Contents

A model that can predict end user satisfaction or QoE (Quality of Experience) directly from the network QoS (Quality of Service) is still illusive in the field of image processing. This motivates the derivation of a meaningful QoS to QoE mapping function to allow one to be predicted in the absence of the other. This paper presents an affine fuzzy logic based model that can estimate the visual perceptual quality for different video content types using a combination of network level and application level QoS parameters. Video contents are classified based on their spatio-temporal feature extraction. The video QoE is predicted in terms of the Mean Opinion Score (MOS). From the results it is clear that the QoE is video content dependent. Also, the network level parameters have more impact on video quality than the application level parameters. Results show that the Fuzzy logic-based model provides high prediction accuracy. The performance of the model was evaluated using a public dataset with good prediction accuracy (~ 95%). The developed model has use in control methods for streaming standard encoded video.

Packet loss visibility across SD, HD, 3D, and UHD video streams

Video quality is analysed by packetization, packet loss, and codec.The paper goes beyond prior studies as it tests SD, HD, 3D, and 4kUHD video.Simulations and network experiments include VBR, CBR, H.264 and HEVC streaming.The paper forecasts bitrates and expected quality in error-prone environments.The impact of packet reordering and duplication is also considered. The trend towards video streaming with increased spatial resolutions and dimensions, SD, HD, 3D, and 4kUHD, even for portable devices has important implications for displayed video quality. There is an interplay between packetization, packet loss visibility, choice of codec, and viewing conditions, which implies that prior studies at lower resolutions may not be as relevant. This paper presents two sets of experiments, the one at a Variable BitRate (VBR) and the other at a Constant BitRate (CBR), which highlight different aspects of the interpretation. The latter experiments also compare and contrast encoding with either an H.264 or an High Efficiency Video Coding (HEVC) codec, with all results recorded as objective Mean Opinion Score (MOS). The video quality assessments will be of interest to those considering: the bitrates and expected quality in error-prone environments; or, in fact, whether to use a reliable transport protocol to prevent all errors, at a cost in jitter and latency, rather than tolerate low levels of packet errors.

Fuzzy logic inference system-based hybrid quality prediction model for wireless 4kUHD H.265-coded video streaming

Networked visual applications such video streaming have grown exponentially in recent years, yet are known to be sensitive to network impairments. However, available measurement techniques that adopt a full reference model are impractical in real-time streaming because they require the original video sequence available at the receivers side. The primary aim of this study is to present a hybrid no-reference prediction model for the perceptual quality of 4kUHD H.265-coded video in the wireless domain. The contributions of this paper are two-fold: first, an investigation of the impact of quality of service (QoS) parameters on 4kUHD H.265-coded video transmission in an experimental environment; second, objective model based on fuzzy logic inference system is developed to predict the visual quality by mapping QoS parameters to the measured quality of experience. The model is evaluated in contrast to random neural networks. The results show that good prediction accuracy was obtained from the proposed hybrid prediction model. This study will help in the development of a reference-free video quality prediction model and QoS control methods for 4kUHD video streaming.

QoE-enabled transport optimisation scheme for real-time SVC video delivery

Video traffic today accounts for 50 percent of all traffic on the network and is set to reach the 70 percent mark in a few years. Bringing in large volumes of video traffic threatens to exceed the networks capacity. As a result, traffic congestion is becoming more frequent, degrading the Quality of Experience (QoE) for video consumers. In this paper, we present a QoE-enabled Transport Optimization Scheme (QETOS) for real-time scalable video stream. The proposed scheme is a crucial design choice that will optimise the video traffic by mapping video quality degradations (that are caused by network) to the QoE without penetrating the video packets. It takes the advantage of the Scalable Video Coding (SVC) partitions that can organize video into layers of different importance, facilitating rate adaptation of video streams. Our approach will help to minimize the side effects on user perceived quality.

Implementation of 4kUHD HEVC-content transmission

The Internet of things (IoT) has received a great deal of attention in recent years, and is still being approached with a wide range of views. At the same time, video data now accounts for over half of the internet traffic. With the current availability of beyond high definition, it is worth understanding the performance effects, especially for real-time applications. High Efficiency Video Coding (HEVC) aims to provide reduction in bandwidth utilisation while maintaining perceived video quality in comparison with its predecessor codecs. Its adoption aims to provide for areas such as television broadcast, multimedia streaming/storage, and mobile communications with significant improvements. Although there have been attempts at HEVC streaming, the literature/implementations offered do not take into consideration changes in the HEVC specifications. Beyond this point, it seems little research exists on real-time HEVC coded content live streaming. Our contribution fills this current gap in enabling compliant and real-time networked HEVC visual applications. This is done implementing a technique for real-time HEVC encapsulation in MPEG-2 Transmission Stream (MPEG-2 TS) and HTTP Live Streaming (HLS), thereby removing the need for multi-platform clients to receive and decode HEVC streams. It is taken further by evaluating the transmission of 4k UHDTV HEVC-coded content in a typical wireless environment using both computers and mobile devices, while considering well-known factors such as obstruction, interference and other unseen factors that affect the network performance and video quality. Our results suggest that 4kUHD can be streamed at 13.5 Mb/s, and can be delivered to multiple devices without loss in perceived quality.

Cross-layer QoE prediction for mobile video based on random neural networks

Based on random neural networks, a cross-layer prediction model is proposed for estimating the perceptual quality of mobile video in no reference mode. The model exploits key parameters affecting video quality. Simulation results show considerable predictability performance with R-squared correlation of 0.90 and 0.39 root mean squared error.

Optimising the delivery of Scalable H.264 Video stream by QoS/QoE correlation

We present a novel system that optimizes the video delivery by mapping network conditions (QoS) to the QoE without penetrating video packets. It takes the advantage of the Scalable Video Coding (SVC) that can organize video streams into different layers. Our approach helps to maximize the QoE with respect to capacity constraints.

Interval Type-2 Fuzzy Logic Quality prediction model for wireless 4kUHD H.265-coded video streaming

This paper proposes a prediction model for the perceptual quality of wireless 4kUHD H.265 video streaming. Based on Interval Type-2 Fuzzy Logic System (IT2FLS), the model exploits application and physical layer parameters. The results show that good prediction accuracy was obtained from the proposed prediction model. This study should help in the development of a reference-free video quality prediction model and QoS control methods for 4kUHD video streaming.

QoE-enabled efficient resource allocation for H.264 video streaming over WiMAX

A power and bandwidth efficient resource allocation approach considering consumers Quality of Experience (QoE) in the context of mobile video streaming is presented. Online QoE is estimated using Fuzzy Inference Systems. Compared to Quality of Service (QoS)-based techniques, the proposed QoE-enabled approach offers considerable saving in bandwidth and power.