Objective assessment of region of interest-aware adaptive multimedia streaming quality

Adaptive multimedia streaming relies on controlled adjustment of content bitrate and consequent video quality variation in order to meet the bandwidth constraints of the communication link used for content delivery to the end-user. The values of the easy to measure network-related Quality of Service metrics have no direct relationship with the way moving images are perceived by the human viewer. Consequently variations in the video stream bitrate are not clearly linked to similar variation in the user perceived quality. This is especially true if some human visual system-based adaptation techniques are employed. As research has shown, there are certain image regions in each frame of a video sequence on which the users are more interested than in the others. This paper presents the Region of Interest-based Adaptive Scheme (ROIAS) which adjusts differently the regions within each frame of the streamed multimedia content based on the user interest in them. ROIAS is presented and discussed in terms of the adjustment algorithms employed and their impact on the human perceived video quality. Comparisons with existing approaches, including a constant quality adaptation scheme across the whole frame area, are performed employing two objective metrics which estimate user perceived video quality

Joint in-network video rate adaptation and measurement-based admission control: algorithm design and evaluation

The important new revenue opportunities that multimedia services offer to network and service providers come with important management challenges. For providers, it is important to control the video quality that is offered and perceived by the user, typically known as the quality of experience (QoE). Both admission control and scalable video coding techniques can control the QoE by blocking connections or adapting the video rate but influence each other's performance. In this article, we propose an in-network video rate adaptation mechanism that enables a provider to define a policy on how the video rate adaptation should be performed to maximize the provider's objective (e.g., a maximization of revenue or QoE). We discuss the need for a close interaction of the video rate adaptation algorithm with a measurement based admission control system, allowing to effectively orchestrate both algorithms and timely switch from video rate adaptation to the blocking of connections. We propose two different rate adaptation decision algorithms that calculate which videos need to be adapted: an optimal one in terms of the provider's policy and a heuristic based on the utility of each connection. Through an extensive performance evaluation, we show the impact of both algorithms on the rate adaptation, network utilisation and the stability of the video rate adaptation. We show that both algorithms outperform other configurations with at least 10 %. Moreover, we show that the proposed heuristic is about 500 times faster than the optimal algorithm and experiences only a performance drop of approximately 2 %, given the investigated video delivery scenario

Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends

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

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

A novel multimedia adaptation architecture and congestion control mechanism designed for real-time interactive applications

PhDThe increasing use of interactive multimedia applications over the Internet has created a problem of congestion. This is because a majority of these applications do not respond to congestion indicators. This leads to resource starvation for responsive flows, and ultimately excessive delay and losses for all flows therefore loss of quality. This results in unfair sharing of network resources and increasing the risk of network ‘congestion collapse’. Current Congestion Control Mechanisms such as ‘TCP-Friendly Rate Control’ (TFRC) have been able to achieve ‘fair-share’ of network resource when competing with responsive flows such as TCP, but TFRC’s method of congestion response (i.e. to reduce Packet Rate) is not ideally matched for interactive multimedia applications which maintain a fixed Frame Rate. This mismatch of the two rates (Packet Rate and Frame Rate) leads to buffering of frames at the Sender Buffer resulting in delay and loss, and an unacceptable reduction of quality or complete loss of service for the end-user. To address this issue, this thesis proposes a novel Congestion Control Mechanism which is referred to as ‘TCP-friendly rate control – Fine Grain Scalable’ (TFGS) for interactive multimedia applications. This new approach allows multimedia frames (data) to be sent as soon as they are generated, so that the multimedia frames can reach the destination as quickly as possible, in order to provide an isochronous interactive service. This is done by maintaining the Packet Rate of the Congestion Control Mechanism (CCM) at a level equivalent to the Frame Rate of the Multimedia Encoder.The response to congestion is to truncate the Packet Size, hence reducing the overall bitrate of the multimedia stream. This functionality of the Congestion Control Mechanism is referred to as Packet Size Truncation (PST), and takes advantage of adaptive multimedia encoding, such as Fine Grain Scalable (FGS), where the multimedia frame is encoded in order of significance, Most to Least Significant Bits. The Multimedia Adaptation Manager (MAM) truncates the multimedia frame to the size indicated by the Packet Size Truncation function of the CCM, accurately mapping user demand to available network resource. Additionally Fine Grain Scalable encoding can offer scalability at byte level granularity, providing a true match to available network resources. This approach has the benefits of achieving a ‘fair-share’ of network resource when competing with responsive flows (as similar to TFRC CCM), but it also provides an isochronous service which is of crucial benefit to real-time interactive services. Furthermore, results illustrate that an increased number of interactive multimedia flows (such as voice) can be carried over congested networks whilst maintaining a quality level equivalent to that of a standard landline telephone. This is because the loss and delay arising from the buffering of frames at the Sender Buffer is completely removed. Packets sent maintain a fixed inter-packet-gap-spacing (IPGS). This results in a majority of packets arriving at the receiving end at tight time intervals. Hence, this avoids the need of using large Playout (de-jitter) Buffer sizes and adaptive Playout Buffer configurations. As a result this reduces delay, improves interactivity and Quality of Experience (QoE) of the multimedia application