A Survey on Multimedia-Based Cross-Layer Optimization in Visual Sensor Networks

Visual sensor networks (VSNs) comprised of battery-operated electronic devices endowed with low-resolution cameras have expanded the applicability of a series of monitoring applications. Those types of sensors are interconnected by ad hoc error-prone wireless links, imposing stringent restrictions on available bandwidth, end-to-end delay and packet error rates. In such context, multimedia coding is required for data compression and error-resilience, also ensuring energy preservation over the path(s) toward the sink and improving the end-to-end perceptual quality of the received media. Cross-layer optimization may enhance the expected efficiency of VSNs applications, disrupting the conventional information flow of the protocol layers. When the inner characteristics of the multimedia coding techniques are exploited by cross-layer protocols and architectures, higher efficiency may be obtained in visual sensor networks. This paper surveys recent research on multimedia-based cross-layer optimization, presenting the proposed strategies and mechanisms for transmission rate adjustment, congestion control, multipath selection, energy preservation and error recovery. We note that many multimedia-based cross-layer optimization solutions have been proposed in recent years, each one bringing a wealth of contributions to visual sensor networks

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

IEEE 802.11 wireless LAN traffic analysis: a cross-layer approach

textThe deployment of broadband wireless data networks, e.g., wireless local area networks (WLANs) [29], experienced tremendous growth in the last several years, and this trend is continuously gaining momentum. In fact, WLAN is becoming an indispensable component of the modern telecommunication infrastructure. Despite this optimistic outlook, however, little is known about the impact of the wireless channel on the characteristics of WLAN traffic. This dissertation characterizes the correlation structures of WLAN channel with traffic statistics from a cross-layer point of view, and provides new measurement methodologies and statistical models for WLAN networks. Currently WLAN standards are designed within the paradigm of the layered network architecture. For example, the architecture of IEEE 802.11 vii is almost identical to the Ethernet. However, wireless networks are fundamentally different from their wired peers due to the shift of transmission media from cables to over-the-air radio waves. This transition exposes wireless systems to the influence of radio propagation, and more importantly, to the temporal and spacial fluctuations of the radio channel that can actually be propagated up to upper layers. However, the current WLAN architecture isolates network layers, and largely ignores this impact. Therefore, we believe that a cross-layer based approach is necessary to understand and reflect this underlying impact of the channel to the upper layers of the network, especially in relation to WLAN traffic behavior. Measurement is one of the fundamental tools used to quantify radio propagation. As part of this dissertation, a complete framework for a measurement methodology, including hardware, software, and measurement procedures, is established. Characteristics of the propagation channel are estimated from measurement data, and the channel knowledge is applied to the upper layers for more realistic and accurate modeling. In WLAN environments, knowledge of the traffic characteristics is essential for proper network provisioning, and for improving the performance of the IEEE 802.11 standard and network devices, e.g., to design improved MAC schemes, or to build better buffer scheduling algorithms with channel knowledge, etc. Built upon extensive WLAN traffic traces, this dissertation work presents cross-layer models for WLAN throughput predictions, traffic statistics, and link layer characteristics. viii The main goal of this dissertation work is to experiment with and develop new methods for identifying channel characteristics. Thereby utilizing this knowledge, we show how to predict and improve WLAN performance. Within the framework of the developed cross-layer measurement methodology, we conducted extensive measurements in different physical environments and different settings such as office buildings and stores, and (1) show that the impact of the propagation channel can be quantified by using simple large scale channel metric (throughput over longer period of time), and (2) also present the existence of a Doppler effect within today’s WLAN packet traffic at sub-second time scales. We also show the real-world WLAN usage pattern from our measurement results. From this data, we conclude that the key issues to study WLAN networks include accurate site-specific propagation channel modeling and real-time autonomous traffic control.Electrical and Computer Engineerin

From statistical- to machine learning-based network traffic prediction

Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large-scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical- and Machine Learning (ML)-based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP.publishedVersio

Scalable Video Content Adaptation

Scalable Video Coding technology enables flexible and efficient distribution of videos through heterogeneous networks. In this regard, the present work proposes and evaluates a method for automatically adapting video contents, according to the available bandwidth. Taking advantage of the scalable video streams characteristics, the proposed solution uses bridge firewalls to perform adaptation. In brief, a scalable bitstream is packetized by assigning a different Type of Service field value, according to the corresponding resolutions. Packets corresponding to the full video resolution are then sent to clients. According to the given bandwidth constraints, an intermediate bridge node, which provides Quality of Service functionalities, eventually discards high resolutions information by using appropriate Priority Queueing filtering policies. A real testbed has been used for the evaluation, proving the feasibility and the effectiveness of the proposed solution