Video Quality Prediction for Video over Wireless Access Networks (UMTS and WLAN)

Transmission of video content over wireless access networks (in particular, Wireless Local Area Networks (WLAN) and Third Generation Universal Mobile Telecommunication System (3G UMTS)) is growing exponentially and gaining popularity, and is predicted to expose new revenue streams for mobile network operators. However, the success of these video applications over wireless access networks very much depend on meeting the user’s Quality of Service (QoS) requirements. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet user’s QoS requirements. Video quality is affected by distortions caused by the encoder and the wireless access network. The impact of these distortions is content dependent, but this feature has not been widely used in existing video quality prediction models. The main aim of the project is the development of novel and efficient models for video quality prediction in a non-intrusive way for low bitrate and resolution videos and to demonstrate their application in QoS-driven adaptation schemes for mobile video streaming applications. This led to five main contributions of the thesis as follows:(1) A thorough understanding of the relationships between video quality, wireless access network (UMTS and WLAN) parameters (e.g. packet/block loss, mean burst length and link bandwidth), encoder parameters (e.g. sender bitrate, frame rate) and content type is provided. An understanding of the relationships and interactions between them and their impact on video quality is important as it provides a basis for the development of non-intrusive video quality prediction models.(2) A new content classification method was proposed based on statistical tools as content type was found to be the most important parameter. (3) Efficient regression-based and artificial neural network-based learning models were developed for video quality prediction over WLAN and UMTS access networks. The models are light weight (can be implemented in real time monitoring), provide a measure for user perceived quality, without time consuming subjective tests. The models have potential applications in several other areas, including QoS control and optimization in network planning and content provisioning for network/service providers.(4) The applications of the proposed regression-based models were investigated in (i) optimization of content provisioning and network resource utilization and (ii) A new fuzzy sender bitrate adaptation scheme was presented at the sender side over WLAN and UMTS access networks. (5) Finally, Internet-based subjective tests that captured distortions caused by the encoder and the wireless access network for different types of contents were designed. The database of subjective results has been made available to research community as there is a lack of subjective video quality assessment databases.Partially sponsored by EU FP7 ADAMANTIUM Project (EU Contract 214751

User-centric QoE-driven vertical handover framework in heterogeneous wireless networks

© 2016 IEEE. With advances in wireless technology and the increase in popularity of mobile devices, more and more people now rely on mobile devices for multimedia services (such as video streaming and video calls). A mobile device can be connected and roamed to different networks in heterogeneous wireless networks. The Media Independent Handover (MIH) framework is designed by the IEEE 802.21 group to support seamless vertical handover between different networks. However, how to select an appropriate network from available ones and when to execute the handover remain the key challenges in MIH. This paper proposes a user-centric QoE-driven vertical handover (VHO) framework, based on MIH, which aims to maintain acceptable QoE of different mobile application services and to select an appropriate network based on users' preferences (e.g. on cost). Further a user-centric QoE-driven algorithm is implemented in the proposed framework. Its performance is evaluated and compared with two other VHO algorithms based on Network Simulator 2 (NS2) for video streaming services over heterogeneous networks. The preliminary results show that the proposed algorithm can maintain better QoE and at the same time, take into account user's preference on cost when compared with the other two algorithms

3D multiple description coding for error resilience over wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.Petroleum Technology Development Fund (PTDF

Quality of experience driven control of interactive media stream parameters

In recent years, cloud computing has led to many new kinds of services. One of these popular services is cloud gaming, which provides the entire game experience to the users remotely from a server, but also other applications are provided in a similar manner. In this paper we focus on the option to render the application in the cloud, thereby delivering the graphical output of the application to the user as a video stream. In more general terms, an interactive media stream is set up over the network between the user's device and the cloud server. The main issue with this approach is situated at the network, that currently gives little guarantees on the quality of service in terms of parameters such as available bandwidth, latency or packet loss. However, for interactive media stream cases, the user is merely interested in the perceived quality, regardless of the underlaying network situation. In this paper, we present an adaptive control mechanism that optimizes the quality of experience for the use case of a race game, by trading off visual quality against frame rate in function of the available bandwidth. Practical experiments verify that QoE driven adaptation leads to improved user experience compared to systems solely taking network characteristics into account

An Adaptive Fuzzy based FEC Algorithm for Robust Video Transmission over Wireless Networks

Forward Error Correction (FEC) is a commonly adopted mechanism to mitigate packet loss/bit error during real-time communication. An adaptive, Fuzzy based FEC algorithm to provide a robust video quality metric for multimedia transmission over wireless networks has been proposed to optimize the redundancy of the generated code words from a Reed-Solomon encoder and to save the bandwidth of the network channel. The scheme is based on probability estimations derived from the data loss rates related to the recovery mechanism at the client end. By applying the adaptive FEC, the server uses the reports to predict the next network loss rate using a curve-fitting technique to generate the optimized number of redundant packets to meet specific residual error rates at the client end. Simulation results in the cellular system show that the video quality is massively adapted to the optimized FEC codes based on the probability of packet loss and packet correlation in a wireless environment

Analyzing the Influence of Smart-device Visual Features, Viewing Distance, and Content Factors on Video Streaming QoE

Quality of experience (QoE) over wireless networks has attracted attention from industry and academia due to an increase in video streaming applications. Several researchers have attempted to understand the factors affecting QoE and design appropriate quality control strategies. Normally, video streaming is initiated by a user who accesses video contents over wireless networks using a smart device held at various viewing distances. Each aforementioned factor has the potential to affect QoE of the viewed session. However, several studies explore the behavior of wireless networks on video streaming QoE. To understand the effects of other factors on QoE, this paper investigates the influence of the device's visual features, viewing distance, and content factors on video streaming. The study adopted an emulation technique to conduct multi-factor experiments designed using the Taguchi method. The 5-ways ANOVA analysis revealed that the effects of smart-device visual features, viewing distance, and content types are significant on video streaming QoE at p<0.05. Moreover, smart devices with a pixel density index of more than 200 ppi produce high QoE, with the viewing distance limited to 45 cm. Lastly, the video bitrate greater than 1024 kbps produced a good QoE regardless of the frame rates

Quality assessment and usage behavior of a mobile voice-over-IP service

Voice-over-IP (VoIP) services offer users a cheap alternative to the traditional mobile operators to make voice calls. Due to the increased capabilities and connectivity of mobile devices, these VoIP services are becoming increasingly popular on the mobile platform. Understanding the user's usage behavior and quality assessment of the VoIP service plays a key role in optimizing the Quality of Experience (QoE) and making the service to succeed or to fail. By analyzing the usage and quality assessments of a commercial VoIP service, this paper identifies device characteristics, context parameters, and user aspects that influence the usage behavior and experience during VoIP calls. Whereas multimedia services are traditionally evaluated by monitoring usage and quality for a limited number of test subjects and during a limited evaluation period, this study analyzes the service usage and quality assessments of more than thousand users over a period of 120 days. This allows to analyze evolutions in the usage behavior and perceived quality over time, which has not been done up to now for a widely-used, mobile, multimedia service. The results show a significant evolution over time of the number of calls, the call duration, and the quality assessment. The time of the call, the used network, and handovers during the call showed to have a significant influence on the users' quality assessments