Although the existing layering infrastructure--used globally for designing computers, data networks, and intelligent distributed systems and which connects various local and global communication services--is conceptually correct and pedagogically elegant, it is now well over 30 years old has started create a serious bottleneck. Using Cross-Layer Techniques for Communication Systems: Techniques and Applications explores how cross-layer methods provide ways to escape from the current communications model and overcome the challenges imposed by restrictive boundaries between layers. Written exclusively by well-established researchers, experts, and professional engineers, the book will present basic concepts, address different approaches for solving the cross-layer problem, investigate recent developments in cross-layer problems and solutions, and present the latest applications of the cross-layer in a variety of systems and networks.
The quality of H.264/AVC compressed video delivery over time-varying and error-prone wireless channels is affected by packet losses. To support quality of service (QoS) for video delivery over wireless networks cross-layer schemes have been discussed in the literature. We introduce a cross-layer priority-aware packet fragmentation scheme at the medium access control (MAC) layer to enhance the quality of pre-encoded H.264/AVC compressed bitstreams over bit-rate limited error-prone links in wireless networks. Larger fragments are more likely to be in error but smaller fragments require more overhead. The H.264 slices are classified in four priorities at the encoder based on their cumulative mean square error (CMSE) contribution towards the received video quality. The slices of a priority class in each frame are aggregated into video packets of corresponding priority at the application (APP) layer. We derive the optimal fragment size for each priority class which achieves the maximum expected weighted goodput at different encoded video bit rates, slice sizes and bit error rates. Priority-aware packet fragmentation invokes slice discard in the buffer due to channel bit rate constraints on allocating fragment header bits. We propose a slice discard scheme using frame importance and slice CMSE contribution to control error propagation effects. Packet fragmentation is then extended to slice fragmentation by modifying the conventional H.264 decoder to handle partial slice decoding. Priority-aware slice fragmentation combined with the proposed slice discard scheme provides considerable peak signal-to-noise ratio (PSNR) and video quality metric gains as compared to priority-agnostic fragmentation. Distortion due to channel errors can be alleviated by assigning stronger channel code rates, at the cost of reduced rate for source coding. Besides MAC layer fragmentation, aggregating H.264/AVC slices at the APP layer to form video packets with sizes adapted to their importance can also improve transmission reliability. We present a cross-layer dynamic programming (DP) approach to minimize the expected received video distortion by jointly addressing the priority-adaptive packet formation at the APP layer and rate compatible punctured convolutional (RCPC) code rate allocation at the physical layer for pre-encoded prioritized slices of each group of pictures (GOP). Our scheme discards some low priority slices in order to improve protection to more important slices and meet the channel bitrate limitations, whenever necessary. Simulation results show that our proposed approach significantly improves received video quality compared to other error protection schemes. Further, we extend our cross-layer DP-based scheme to slices of each frame by predicting the expected channel bit budget per frame for real-time transmission. The prediction uses a generalized linear model developed over the parameters - CMSE per frame, channel SNR, and normalized compressed frame bit budget determined over a video dataset that spans high, medium and low motion complexity. This predicted frame bit budget is used to derive the packet sizes and their corresponding RCPC code rates for transmission using our DP-based approach. Simulation results show good correlation with the results of our DP-based scheme applied over the GOP. Unique characteristics of video traffic, such as the temporal and spatial dependencies between different video frames and their deadline constraints, pose a challenge in supporting the video quality rendered to the clients over time-varying, bandwidth-limited channels. Scalable Video Coding (H.264/SVC) enables the transmission and decoding of partial bit streams to provide video services with lower temporal or spatial resolutions or reduced fidelity while retaining a reconstruction quality that is high relative to the rate of the partial bit streams. We propose a sliding-window based flow control for scheduling the network abstraction layer (NAL) units in the post-encoding buffer of the streaming server for a real-time scalable video transmission scenario over a fast time-varying channel. Our scheduling scheme considers the importance of the NAL unit in terms of (i ) its CMSE distortion contributed to the received video quality, (ii ) its size in bits, and (iii ) its time-to-expiry in seconds. The scheduling problem of determining the appropriate order of transmission is formulated as a 0-1 knapsack problem and a DP solution is proposed which runs in polynomial time. Our scheduling approach significantly reduces the number of whole frames discarded as compared to (a) a CMSE-based scheme which considers the importance of the NAL units only in terms of their CMSE contribution, and (b) the earliest deadline first scheme which minimizes the dwelling time of the NAL units in the post-encoding buffer. Simulation results show significant PSNR gains for different video sequences at different pre-roll delays.
Rapid growth of video applications over wireless networks is overwhelming the wireless bandwidth. Since video applications demand large bandwidth and realtime transmission, supporting the rapidly increasing video traffic over the bandwidth-limited, error-prone, and time-varying wireless channels is very challenging. As a result, the video applications are likely to suffer packet losses over wireless networks which results in quality degradation. In this dissertation, we design a distortion prediction model for H.264/AVC compressed video streams, and use it for designing novel cross-layer protocols for enhancing the video quality by making more efficient use of the available wireless resources. The cumulative mean squared error (CMSE) is a widely used measure of video distortion. However, CMSE measurement is a time-consuming and computationally-intensive process which is not suitable for many video applications. A low-complexity and low-delay generalized linear model is proposed for predicting CMSE contributed by the loss of H.264 AVC encoded video slices. The model is trained over a video database by using a combination of video factors that are extracted during the encoding of the current frame, without using any data from future frames in the group of pictures (GOP). The slices are then prioritized within a GOP based on their predicted CMSE values. The accuracy of the CMSE prediction model is analyzed using cross-validation, analysis of variance, and correlation coefficients. The simulations are carried out to evaluate the performance of the CMSE prediction model for varying encoder configurations and bit rates of test videos. The CMSE slice prediction model is used to design an unequal error protection (UEP) scheme, using the rate-compatible punctured convolutional (RCPC) codes over wireless channels. This scheme provides protection to the video slices against the channel errors, based on their priority, in order to minimize the video distortion. An application of our slice prioritization is demonstrated by implementing a priority-aware slice discard scheme, where the low-priority slices are dropped from the router when the network experiences congestion. Additionally, the GOP-level slice prioritization is extended to the frame-level slice prioritization, and its performance is evaluated over the additive white Gaussian noise (AWGN) channels The idea of using slice CMSE prediction is extended to adapt the video packet size to the wireless channel conditions, in order to minimize the video distortion. A real-time, priority-aware joint packet fragmentation and error protection scheme for real-time video transmission over Rayleigh fading channels is presented. The fragment error rates (FERs) are simulated for a combination of different fragment sizes and RCPC code rates. These FERs are then used to determine the optimal fragment sizes and code rates for packets of each priority class by minimizing the expected normalized predicted CMSE per GOP in H.264 video bit stream. An improvement in the received video quality over the conventional and priority-agnostic packet fragmentation schemes is observed. Next, a cross-layer, priority-aware scheduling scheme for real-time transmission of multiple video applications over a time-varying channel is developed. Each video application considered has different characteristics such as user priority, latency, distortion, size, and encoding bit rate. A cost function is optimized to determine the scheduling order for video frames. The performance of our scheme is compared with that of the CMSE based scheme, where the frames are rank-ordered for transmission using its CMSE per bit values, and with the earliest deadline first (EDF) scheme in which each user takes turns to transmit a frame. A collaborative effort with other researchers and developed two additional cross-layer error protection schemes. In the first scheme, a cross layer UEP scheme that jointly assigned FEC at both the Application layer (using Luby Transform) and the Physical layer (using RCPC codes) for prioritized video transmission is developed. The video distortion function is minimized by using the genetic algorithm (GA). The performance of our scheme is evaluated for different channel SNR values. In the second UEP scheme, a framework that combined the RCPC codes and concatenated it with hierarchical quadrature amplitude modulation (QAM) is investigated. Employing RCPC codes and hierarchical modulation jointly resulted in greater flexibility as some parts of the data can be protected only by the hierarchical modulation while others may be protected by a low FEC code rate. The performance of the proposed scheme is compared to the standard 8-QAM with symmetric constellation.
It is well known that the modern day wireless networks involve a significant amount of video traffic. The present day physical layer standards support high bit-rates so that the HD video could be streamed. But though the bit-rates are high, sudden channel errors are unexpected and unavoidable. This reflects on the video quality very badly. This means there is still room for intelligent solution for efficient video transfer. Cross-layer approach is one of the solution. Due to presence of frame dependencies in video the frames are prioritized and assigned a relative priority index values which is used to provide unequal error protection. Also the transmission deadlines of each frame is different. Thus a time based adaptive MAC layer re-transmission is proposed in this thesis. In this thesis we mainly focus on developing a cross-layer framework for wireless unicast video transmission on software defined radios (SDR). SDR are being very flexible in implementing MAC and PHY layers, helps to easily implement a cross-layer design too. A MAC layer centric cross-layer approach is adopted here. This approach involves choosing an appropriate modulation scheme with the known knowledge of channel state available from PHY layer at the receiver, and relative priority index (RPI) of each video frame from the application layer and adapting MAC re-transmission based on transmission deadline (Td) of the video packet from APP layer. An in-band cross-layer signaling is adopted here, in which RPI and Td are written in the header of the rtp packet. The SNR values from receiver are sent back to the sender along with the MAC acknowledgment. A content aware time based adaptive re-transmission (TAR) and a heuristic modulation selection algorithm are implemented using this framework. MAC and PHY layers are developed on GNU radio with USRPs N210s. Evalvid tool at the application layer for video streaming and evaluation. Results show that the gain in the efficiency is mostly due to the TAR mechanism.
Last few years have seen rapid acceptance of high-definition television (HDTV) technology around the world. This technology has been hugely successful in delivering more realistic television experience at home and accurate imaging for professional applications. Adoption of high definition continues to grow as consumers demand enhanced features and greater quality of content. Following this trend, natural evolution of visualisation technologies will be in the direction of fully realistic visual experience and highly precise imaging. However, using the content of even higher resolution and quality is not straightforward as such videos require significantly higher access bandwidth and more processing power. Therefore, methods for radical reduction of video bandwidth are crucial for realisation of high visual quality. Moreover, it is desirable to look into other ways of accessing visual content, solution to which lies in innovative schemes for content delivery and consumption. This book presents selected chapters covering technologies that will enable greater flexibility in video content representation and allow users to access content from any device and to interact with it.
"This book spans a number of interdependent and emerging topics in streaming media, offering a comprehensive collection of topics including media coding, wireless/mobile video, P2P media streaming, and applications of streaming media"--Provided by publisher.
Bandwidth-intensive video streaming applications occupy an overwhelming fraction of bandwidth-limited wireless network traffic. Compressed video data are highly structured and the psycho-visual perception of distortions and losses closely depends on that structure. This dissertation exploits the inherent video data structure to develop perceptually-optimized transmission paradigms at different protocol layers that improve video quality of experience, introduce error resilience, and enable supporting more video users. First, we consider the problem of network-wide perceptual quality optimization whereby different video users with (possibly different) real-time delay constraints are sharing wireless channel resources. Due to the inherently stochastic nature of wireless fading channels, we provide statistical delay guarantees using the theory of effective capacity. We derive the resource allocation policy that maximizes the sum video quality and show that the optimal operating point per user is such that the rate-distortion slope is the inverse of the supported video source rate per unit bandwidth, termed source spectral efficiency. We further propose a scheduling policy that maximizes the number of scheduled users that meet their QoS requirement. Next, we develop user-level perceptual quality optimization techniques for non-scalable video streams. For non-scalable videos, we estimate packet loss visibility through a generalized linear model and use for prioritized packet delivery. We solve the problem of mapping video packets to MIMO subchannels and adapting per-stream rates to maximize the total perceptual value of successfully delivered packets per unit time. We show that the solution enables jointly reaping gains in terms of improved video quality and lower latency. Optimized packet-stream mapping enables transmission of more relevant packets over more reliable streams while unequal modulation opportunistically increases the transmission rate on the stronger streams to enable low latency delivery of high priority packets. Finally, we develop user-level perceptual quality optimization techniques for scalable video streams. We propose online learning of the mapping between packet losses and quality degradation using nonparametric regression. This quality-loss mapping is subsequently used to provide unequal error protection for different video layers with perceptual quality guarantees. Channel-aware scalable codec adaptation and buffer management policies simultaneously ensure continuous high-quality playback. Across the various contributions, analytic results as well as video transmission simulations demonstrate the value of perceptual optimization in improving video quality and capacity.
"This book further explores various issues and proposed solutions for the provision of Quality of Service (QoS) on the wireless networks"--Provided by publisher.
Communication, Management and Information Technology contains the contributions presented at the International Conference on Communication, Management and Information Technology (ICCMIT 2016, Cosenza, Italy, 26-29 April 2016, organized by the Universal Society of Applied Research (USAR). The book aims at researchers, scientists, engineers, and scholar students interested or involved in Computer Science and Systems, Communication, and Management.
