Holographic-type communication brings an immersive tele-holography experience by delivering holographic contents to users. As the direct representation of holographic contents, hologram videos are naturally three-dimensional representation, which consist of a huge volume of data. Advanced multi-connectivity (MC) millimeter-wave (mmWave) networks are now available to transmit hologram videos by providing the necessary bandwidth. However, the existing link selection schemes in MC-based mmWave networks neglect the source content characteristics of hologram videos and the coordination among the parameters of different protocol layers in each link, leading to sub-optimal streaming performance. To address this issue, we propose a cross-layer-optimized link selection scheme for hologram video streaming over mmWave networks. This scheme optimizes link selection by jointly adjusting the video coding bitrate, the modulation and channel coding schemes (MCS), and link power allocation to minimize the end-to-end hologram distortion while guaranteeing the synchronization and quality balance between real and imaginary components of the hologram. Results show that the proposed scheme can effectively improve the hologram video streaming performance in terms of PSNR by 1.2 dB to$\mathbf{6. 4 d B}$against the non-cross-layer scheme.

Eavesdroppers of wireless signals want to infer as much as possible regarding the transmitter (Tx). Popular methods to minimize information leakage to the eavesdropper include covert communication, directional modulation, and beamforming with nulling. In this paper we do not attempt to prevent information leakage to the eavesdropper like the previous methods. Instead we propose to beamform the wireless signal at the$\mathbf{T x}$in such a way that it incorporates deceptive information. The beamformed orthogonal frequency division multiplexing (OFDM) signal includes a deceptive value for the Doppler (velocity) and range of the Tx. To design the optimal baseband waveform with these characteristics, we define and solve an optimization problem for power-efficient deceptive wireless beamforming (DWB). The relaxed convex Quadratic Program (QP) is solved using a heuristic algorithm. Our simulation results indicate that our DWB scheme can successfully inject deceptive information with low power consumption, while preserving the shape of the created beam.

This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise and inference mitigation, while the subsequent Hankel-DMD stage enables reliable parameter extraction even for overlapping sginals or strong interference conditions. Numerical simulations demonstrate the robustness of MAHankDMD, showing its ability to precisely estimate both DOA and frequency under challenging conditions involving RFI and Gaussian white noise interference. The proposed algorithm thus provides an effective solution for channel parameter estimation in complex noisy environments.

Robust keypoint detection on omnidirectional images against large perspective variations, is a key problem in many computer vision tasks. In this paper, we propose a perspectively equivariant keypoint learning framework named OmniKL for addressing this problem. Specifically, the framework is composed of a perspective module and a spherical module, each one including a keypoint detector specific to the type of the input image and a shared descriptor providing uniform description for omnidirectional and perspective images. In these detectors, we propose a differentiable candidate position sorting operation for localizing keypoints, which directly sorts the scores of the candidate positions in a differentiable manner and returns the globally top-K keypoints on the image. This approach does not break the differentiability of the two modules, thus they are end-to-end trainable. Moreover, we design a novel training strategy combining the self-supervised and co-supervised methods to train the framework without any labeled data. Extensive experiments on synthetic and real-world 360° image datasets demonstrate the effectiveness of OmniKL in detecting perspectively equivariant keypoints on omnidirectional images. Our source code are available online at https://github.com/vandeppce/sphkpt.

The application of deep neural networks (DNNs) on 360-degree images has achieved remarkable progress in the recent years. However, DNNs have been demonstrated to be vulnerable to well-crafted adversarial examples, which may trigger severe safety problems in the real-world applications based on 360-degree images. In this paper, we propose an adversarial attack targeting spherical images, called 360-attactk, that transfers adversarial perturbations from perspective-view (PV) images to a final adversarial spherical image. Given a target spherical image, we first represent it with a set of planar PV images, and then perform 2D attacks on them to obtain adversarial PV images. Considering the issue of the projective distortion between spherical and PV images, we propose a distortion-aware attack to reduce the negative impact of distortion on attack. Moreover, to reconstruct the final adversarial spherical image with high aggressiveness, we calculate the spherical saliency map with a novel spherical spectrum method and next propose a saliency-aware fusion strategy that merges multiple inverse perspective projections for the same position on the spherical image. Extensive experimental results show that 360-attack is effective for disturbing spherical images in the black-box setting. Our attack also proves the presence of adversarial transferability from Z2 to SO(3) groups.

Object detection methods for perspective images have proven increasingly efficient, but the techniques for equirectangular projection (ERP) panoramas from inherently spherical imaging cannot still achieve satisfactory performance. Due to the various degrees of distortion at different pixel locations, current algorithms cannot adapt to the changes in shape and contour caused by stretching, which results in performance degradation when migrating them from perspective images to spherical ones. In this paper, we improve the network for panorama object detection and introduce the cube-domain information with discontinuity but low distortion to correct the panorama features. Unlike previous works, we consider the impact of semantic discontinuity from all tangent planes instead of overlaying features when needed. Considering the six facets as unified, i.e., six-to-one for extraction, the proposed Facet-Link module enhances the long-range sensing capability at the facet level in the frequency domain. Moreover, the position alignment packs different facets, i.e., six-to-one for calibration, to preserve more global signals during the correction stage, which establishes semantic pathways for feature interactions between panorama and cubemap in the two dimensions, facet-facet and cube-pano, respectively. Extensive experiments on synthetic and real-world datasets verify the effectiveness and robustness of our proposed method.

To conduct service quality management of industry devices or Internet infrastructures, various deep learning approaches have been used for extracting the normal patterns of multivariate Key Performance Indicators (KPIs) for unsupervised anomaly detection. However, in the scenario of Content Delivery Networks (CDN), KPIs that belong to diverse websites usually exhibit various structures at different timesteps and show the non-stationary sequential relationship between them, which is extremely difficult for the existing deep learning approaches to characterize and identify anomalies. To address this issue, we propose a switching Gaussian mixture variational recurrent neural network (SGmVRNN) suitable for multivariate CDN KPIs. Specifically, SGmVRNN introduces the variational recurrent structure and assigns its latent variables into a mixture Gaussian distribution to model complex KPI time series and capture the diversely structural and dynamical characteristics within them, while in the next step it incorporates a switching mechanism to characterize these diversities, thus learning richer representations of KPIs. For efficient inference, we develop an upward-downward autoencoding inference method which combines the bottom-up likelihood and up-bottom prior information of the parameters for accurate posterior approximation. Extensive experiments on real-world data show that SGmVRNN significantly outperforms the state-of-the-art approaches according to F1-score on CDN KPIs from diverse websites.

As one type of omnidirectional projection, fisheye images have been widely used in automatic driving and visual surveillance. However, they cannot be processed well by the traditional algorithms designed for the planar rectilinear images since they usually suffer from severe geometric distortion during image formation. In this paper, the conventional face detection algorithm is enhanced to fit the fisheye images via combining with the spherical convolution block by learning rotation-invariant features from the spherical domain. The learned features from both planar and spherical domains are subsequently mixed by the spatial attention mechanism. Consequently, the whole network can automatically learn the distorted features directly from different positions on the target image. Experimental results verify that our network can detect distorted faces on fisheye images effectively and maintain the performance on traditional planar images.

With the rapid advancements in Augmented Reality, the number of AR users is gradually increasing and the multiuser AR ecosystem is on the rise. Currently, AR applications usually present results without limitations, which causes great latent danger to users, so it is necessary to apply strategies to ensure the safe output of AR. Due to the environmental diversities among the distributed users, the traditional approaches designed for single-user AR are not efficient for multi-user AR applications. Considering the characteristics of multi-user AR scenarios, we propose a multi-user AR output strategy model based on Federated Reinforcement Learning. With the device-fog-cloud hierarchical architecture, the proposed models are obtained first by Reinforcement Learning on users' devices, and are then hierarchically aggregated on the fog nodes and cloud server. We performed extensive AR simulations in Unity and obtained the results that show our method can avoid several security problems existent in multi-user AR applications.

With the proliferation of edge intelligence and the breakthroughs in machine learning, Federated Learning (FL) is capable of learning a shared model across several edge devices by preserving their private data from being exposed to external adversaries. However, the distributed architecture of FL naturally introduces communication between the central parameter server and the distributed learning nodes. The huge communication cost poses a challenge to practical FL, especially for FL in mobile edge computing (MEC) networks. Existing communication-efficient FL systems predominantly optimize their intrinsic learning process and are not concerned with the implications on the network. In this paper we propose a FL scheme that leverages Device-to-Device (D2D) communication (hence called D2D-FedAvg) and is suitable for mobile edge networks. D2D-FedAvg creates a two-tier learning model where D2D learning groups communicate their results as a single entity to the MEC server leading to traffic reduction. We propose the schemes for D2D grouping, master UE selection, and also D2D exit in the learning process and then form a complete D2D-assisted federated averaging algorithm. Via extensive simulations on the Federated Extended MNIST dataset, the feasibility and convergence of D2D-FedAvg scheme are evaluated. Our results show that D2D-FedAvg lowers the communication cost relative to the typical Federated Averaging (FedAvg) in cellular networks as the number of users is increased (for 100 cellular users 37% traffic reduction), while keeping the same learning accuracy with FedAvg across the board.

Wireless communications are vulnerable against radio frequency (RF) interference which might be caused either intentionally or unintentionally. A particular subset of wireless networks, Vehicular Ad-hoc NETworks (VANET), which incorporate a series of safety-critical applications, may be a potential target of RF jamming with detrimental safety effects. To ensure secure communications between entities and in order to make the network robust against this type of attacks, an accurate detection scheme must be adopted. In this paper, we introduce a detection scheme that is based on supervised learning. The k-nearest neighbors (KNN) and random forest (RaFo) methods are used, including features, among which one is the metric of the variations of relative speed (VRS) between the jammer and the receiver. VRS is estimated from the combined value of the useful and the jamming signal at the receiver. The KNN-VRS and RaFo-VRS classification algorithms are able to detect various cases of denial-of-service (DoS) RF jamming attacks and differentiate those attacks from cases of interference with very high accuracy.

Similar to conventional video, the increasingly popular 360 virtual reality (VR) video requires copyright protection mechanisms. The classic approach for copyright protection is the introduction of a digital watermark into the video sequence. Due to the nature of spherical panorama, traditional watermarking schemes that are dedicated to planar media cannot work efficiently for 360 VR video. In this article, we propose a spherical wavelet watermarking scheme to accommodate 360 VR video. With our scheme, the watermark is first embedded into the spherical wavelet transform domain of the 360 VR video. The spherical geometry of the 360 VR video is used as the host space for the watermark so that the proposed watermarking scheme is compatible with the multiple projection formats of 360 VR video. Second, the just noticeable difference model, suitable for head-mounted displays (HMDs), is used to control the imperceptibility of the watermark on the viewport. Third, besides detecting the watermark from the spherical projection, the proposed watermarking scheme also supports detecting watermarks robustly from the viewport projection. The watermark in the spherical domain can protect not only the 360 VR video but also its corresponding viewports. The experimental results show that the embedded watermarks are reliably extracted both from the spherical and the viewport projections of the 360 VR video, and the robustness of the proposed scheme to various copyright attacks is significantly better than that of the competing planar-domain approaches when detecting the watermark from viewport projection.

In recent years, Downlink (DL) throughput estimation in Mobile Broadband (MBB) networks has gained immense popularity and it is expected to become a vital component of the upcoming fifth generation (5G) systems. Plentiful adaptive video streaming algorithms greatly rely on accurate DL throughput predictions to adapt their mechanisms and ensure high Quality of Service (QoS) to the end-users. Thus far, conventional DL throughput estimation approaches, also known as speed tests, require an extensive exchange of TCP traffic over the network for an allocated time duration. While such tools appear to deliver trustworthy results, they turn out to be inefficient when mobile subscriptions with limited data plans are engaged. In this paper, we propose a supervised Machine Learning (ML) solution for DL throughput estimation that aims at delivering highly accurate predictions while significantly limiting the over-the-air data consumption. We capture the network performance metrics by exploring both crowdsourced and controlled testing methodologies. We leverage RTR-NetTest, a platform of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), and MONROE-NetTest, its counterpart wrapper built as an Experiment as a Service (EaaS) on top of Measuring Mobile Broadband Networks in Europe (MONROE). Results reveal that our solution can achieve a 39.7% reduction in terms of data consumption while delivering a Median Absolute Percentage Error (MdAPE) of 5.55%. We further show that accuracy can be traded-off, for example, a significant data consumption reduction of 95.15% can be achieved for a MdAPE of 20%.

Vehicular Ad Hoc Networks (VANETs) aim at enhancing road safety and providing a comfortable driving environment by delivering early warning and infotainment messages to the drivers. Jamming attacks, however, pose a significant threat to their performance. In this paper, we propose a novel Relative Speed Estimation Algorithm (RSEA) of a moving vehicle that approaches a transmitter ( Tx )-receiver ( Rx ) pair that interferes with their radio frequency (RF) communication by conducting a denial of service (DoS) attack. Our scheme is completely passive and uses a pilot-based received signal without hardware or computational cost to, firstly, estimate the combined channel between the transmitter-receiver and jammer-receiver and, secondly, to estimate the jamming signal and the relative speed between the jammer-receiver using the RF Doppler shift. Moreover, the relative speed metric exploits the angle of projection (AOP) of the speed vector of the jammer in the axis of its motion in order to form a two-dimensional representation of the geographical area. Our approach can effectively be applied for any form of the jamming signal and is proven to have quite accurate performance, with a mean absolute error (MAE) value of approximately 10% compared to the optimal zero MAE value under different jamming attack scenarios.

Distributed caching architectures have been proposed for bringing content close to requesters, and the key problem is to design caching algorithms for reducing content delivery delay, which determines to an extent the user Quality of Experience (QoE). This problem obtains an interesting new twist with the advent of advanced layered-video encoding techniques such as Scalable Video Coding. In this paper, we show that the problem of finding the caching configuration of video encoding layers that minimizes delivery delay for a network operator is NP-Hard, and we establish a pseudopolynomial-time optimal solution by using a connection with the multiple-choice knapsack problem. Next, we design caching algorithms for multiple network operators that cooperate by pooling together their co-located caches, in an effort to aid each other, so as to avoid large delays due to fetching content from distant servers. We derive an approximate solution to this cooperative caching problem by using a technique that partitions the cache capacity into amounts dedicated to own and other operators' caching needs. Trace-driven evaluations demonstrate up to 25 percent reduction in delay over existing caching schemes. As a side benefit, our algorithms achieve smoother playback for video streaming applications, with fewer playback stalls and higher decoded quality.

Panoramic virtual reality video (PVRV) is becoming increasingly popular since it offers a true immersive experience. However, the ultra-high resolution of PVRV requires significant bandwidth and ultra-low latency for PVRV streaming, something that makes challenging the extension of this application to mobile networks. Besides bandwidth, the frequent perspective viewport rendering induces a heavy computational load on battery-constrained mobile devices. To attack these problems jointly, this paper proposes a PVRV streaming system that is designed for modern multiconnectivity-based millimeter wave (mmWave) cellular networks in conjunction with mobile edge computing (MEC). First, mmWave is deployed to support the high bandwidth needs of PVRV streaming. Next, the multiple mmWave links that tend to suffer from outages are coupled with a sub-6 GHz link to ensure disruption-free wireless communication. With the help of an MEC server, the tradeoff among link adaptation, transcoding-based chunk quality adaptation, and viewport rendering offloading is sought to improve the wireless bandwidth utilization and mobile device's energy efficiency. Simulation results show that the proposed scheme can improve the streaming performance in both energy efficiency and the quality of received viewport over the state-of-the-art schemes.

Hyperparameter optimization is an important but often ignored part of successfully training Neural Networks (NN) since it is time consuming and rather complex. In this paper, we present HINDSIGHT, an open-source framework for designing and implementing NN that supports hyperparameter optimization. HINDSIGHT is built entirely in R and the current version focuses on Long Short Term Memory (LSTM) networks, a special kind of Recurrent Neural Networks (RNN). HINDSIGHT is designed in a way that it can easily be expanded to other types of Deep Learning (DL) algorithms such as Convolutional Neural Networks (CNN) or feed-forward Deep Neural Networks (DNN). The main goal of HINDSIGHT is to provide a simple and quick interface to get started with LSTM networks and hyperparameter optimization.

In the chain of stereoscopic video processing, stereoscopic video coding and viewing are usually two independent stages. Conventional stereoscopic video coding puts an emphasis on improving the coding efficiency by seeking the optimal tradeoff between the coding bit rate and the signal-based distortion, while neglecting the perceptual behaviors of binocular combination when stereoscopic video is viewed by human beings. In this paper, we propose to utilize binocular combination to optimize the stereoscopic video coding from the perspective of perceptual quality measurement. Specifically, we propose a novel binocular-combination-oriented measurement for visual distortion and then derive the Lagrange multiplier for the binocular-combination-oriented rate-distortion optimization (RDO). Via extensive subjective tests, the results show that the proposed perceptual RDO can save more than 5% BD rate over the traditional RDO in multiview extension of High Efficiency Video Coding for stereoscopic video coding.

IP networks have become the dominant platform for video delivery. However, bandwidth-hungry video is pushing networks to their limits: costs are rising for the operators and the viewing experience is not always satisfactory for the users. When considering 3D video delivery, the previous problems are exacerbated because of the higher volume of data that must be communicated, and the difficulty in characterizing the viewing experience of the end user. Consequently, network operators may be reluctant to deliver 3D video due to costs and unclear quality improvements to their users. In this setting, the true immersive experience of 3D video remains elusive. In this paper, we focus on the efficient delivery of 3D video in terms of quality and energy cost over centrally controlled networks. As a representative example of a centrally controlled network, a software-defined network (SDN) is assumed. Our approach is based on a comprehensive network-dependent 3D quality of experience (3DQoE) model and an energy cost model for 3D video streaming. By using the developed models, we formulate the problem of energy-efficient and 3DQoE-optimized 3D video flow path routing. The particular characteristic of video/depth rate allocation presented in 3D video is embedded seamlessly into the selection of the optimal routing paths for multiple 3D video streams. The formulated problem is NP-hard and is solved with a heuristic algorithm based on the branch-and-bound method after significant reduction of the solution search space. Extensive 3D video streaming experiments are conducted over an OpenFlow-based SDN with subjective and objective evaluations and they highlight the significant benefits of the proposed approach.

With the increasing capacity of wireless networks, more people would like to consume the 360-degree panoramic video (PV) in virtual reality (VR) applications as its immersive experience. However, due to the super-high resolution of the PV and the dynamic features of wireless networks, it is very difficult to efficiently deliver PVs over wireless networks. The traditionally independent PV encoding and networking sometimes also results in the PV quality deterioration since it neglects the harmony between the source encoding and networking. In this paper, a joint source encoding and networking optimization scheme is proposed to transmit the PV over LTE-A downlink. The PV encoding parameters during the source compression, the modulation and coding scheme (MCS), and relay selection during the networking are jointly considered to optimize the end-to-end PV quality. In addition, the video quality for region of interest (RoI, the possible viewport region) is enhanced by allowing a larger latency bound in the joint source encoding and networking optimization. Experimental results show that the proposed scheme achieves significant performance improvement for the quality of the received PV over traditional PV streaming approaches.

Crowdsourcing mobile network performance evaluation is rapidly gaining popularity, with new applications aiming to deliver more accurate and reliable results every day. From the perspective of end-users, these utilities help them estimate the performance of their service provider in terms of throughput, latency and other key performance indicators of the network. In this paper, we build ORCA: Operator Classifier, a Machine Learning (ML) based framework to define and determine the behavior of Mobile Network Operators (MNOs) from crowdsourced datasets. We investigate whether one can differentiate MNOs by using crowdsourced end-to-end network measurements. We consider different performance metrics (e.g. Download (DL)/Upload (UL) data rate, latency, signal strength) and study the impact of them individually but also collectively on differentiating MNOs. We use RTR Open Data, an open dataset of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), to characterize the three major mobile native operators and two virtual operators in Austria. Our results show that ORCA can be used to identify patterns between various mobile systems and disclose their differences from the end-user perspective.

Distributed caching architectures have been proposed for bringing content close to requesters and the key problem is to design caching algorithms for reducing content delivery delay. The problem obtains an interesting new twist with the advent of advanced layered-video encoding techniques such as Scalable Video Coding (SVC). We show that the problem of finding the caching configuration of video encoding layers that minimizes average delay for a network operator is NP-Hard, and we establish a pseudopolynomial-time optimal solution using a connection with the multiple-choice knapsack problem. We also design caching algorithms for multiple operators that cooperate by pooling together their co-located caches, in an effort to aid each other, so as to avoid large delays due to downloading content from distant servers. We derive an approximate solution to this cooperative caching problem using a technique that partitions the cache capacity into amounts dedicated to own and others' caching needs. Numerical results based on real traces of SVC-encoded videos demonstrate up to 25% reduction in delay over existing (layer-agnostic) caching schemes, with increasing gains as the video popularity distribution gets steeper, and cache capacity increases.

With a wide variety of mobile applications and social networks, there is a growing demand for anytime, anywhere access to high quality video and other multimedia content. Fifth generation wireless networks are aiming to keep up with user demands by incorporating new spectrum bands at high frequencies, which results in shrinking of cell sizes and eventually leads to small cells. Dense deployment of small cells and base stations open up new opportunities for faster and energy-efficient access to content. Content caching at small cell base stations, for the purpose of reducing delay, has been studied in several previous works. In our previous work, we used Integer Linear Programming (ILP) models to show that content caching can also reduce energy consumption of User Equipment (UE). In particular, selecting a subset of small cell base stations to cache the content, reduces uplink energy budget, cuts down caching energy budget and simplifies management of distributed caches. Yet, ILP based methods are powerful for obtaining benchmark results but they are computationally intensive for large size problems. In this paper, we propose a particle swarm optimization (PSO) based scheme to deploy an overlay small cell content-caching network with the aim of reducing UE energy consumption. We show that PSO is computationally efficient while it slightly increases energy consumption with respect to the ILP-based methods.

Video services are proliferating over today's mobile Internet and efforts have been made to improve their performance. The cross-layer video streaming that jointly optimizes the parameters at different protocol layers is a feasible solution but without bandwidth aggregation of multiple wireless networks. The cross-network video streaming can realize the bandwidth aggregation by using multiple overlapping wireless networks simultaneously. However, parameters at different protocol layers are optimized independently in the cross-network optimization. In this paper, we propose a joint cross-network and cross-layer optimized video streaming scheme that utilizes the bandwidth aggregation of cross-network video streaming and further improve the performance by jointly optimizing the parameters of different protocol layers in each network with a cross-layer manner. In the proposed scheme, the LTE and WCDMA networks are adopted. The bit-rate of the video at application layer, the rate allocation among networks and the parameters of physical layers in each network are jointly optimized. Experimental results show that the proposed scheme gains higher quality of experience in terms of PSNR than the state-of-the-art schemes.

In this paper, we present a cooperative video multicast protocol for densely deployed small cells. We assume that the small cell Base Stations (BS) have overlapping coverages and they are allowed to have interfering transmissions. The proposed cooperative multicast protocol realizes a two-hop transmission system where each hop has its own time slot. In the first hop, the small cell base stations simultaneously transmit the packets to the relays who are also the users of multicast transmission. The relays then follow the optimal strategy under interference conditions and apply successive interference cancellation (SIC) decoding. In the second hop, the relays apply a Distributed Space Time Code (DSTC) that dynamically adapts itself based on the result of SIC and broadcast the result. This cooperative transmission scheme is complemented with application layer Forward Error Correction (FEC) in order to handle the remaining packet level losses. Through extensive simulations, we investigate the performance of the proposed scheme and show that the proposed protocol can efficiently handle inter-user interference, leading to superior performance over the state-of-the-art purely orthogonal cooperative transmission schemes.

In this paper, we investigate low-complexity, receiver-driven strategies for the transmission of two correlated sources in a multiple access quasi-static fading channel. We consider two different transmission modes at the physical (PHY) layer depending on the average signal to noise ratio (SNR) of the involved Rayleigh fading channels. The two modes that are allowed are orthogonal transmissions and interfering transmissions with successive interference cancellation (SIC) decoding. Further, we consider the sources are compressed independently while they are jointly decoded with an adaptive linear minimum mean square error (MMSE) algorithm. Hence, the receiver selects the combination of a PHY transmission mode that should be used at the sources, together with the adaptive MMSE-based estimation of each source. We show that in different SNR regimes, a different transmission strategy is optimum, that is in the low SNR regime interfering transmission together with MMSE decoding is better, while in the high SNR regime orthogonal transmission is superior. Both schemes outperform distributed source coding (DSC) based approaches regardless of the degree of the correlation of the two sources.

In this paper, we consider Distributed Estimation (DES) in a Wireless Sensor Network (WSN) and assume that the number of sensors in the WSN is larger than the available number of transmission slots. With classic DES, the sensors independently transmit the sampled digitized data. However, the WSN is an uplink multiuser channel where multiple sources share the channel for communicating data to a Fusion Center (FC). To this aim, we adopt the optimal communication scheme for this setup that suggests interfering transmissions and the use of Successive Interference Cancelation (SIC) at the FC. We propose a joint SIC decoder and linear Minimum-Mean-Square-Error (MMSE) estimator for digital interfering transmission of correlated data. We further introduce an optimization framework that schedules and allocates power to the sensors optimally. We formulate the problem in two ways: an expected distortion minimization problem under a total power budget, and a transmission power minimization problem under a distortion constraint. For both cases, we consider the system performance under different operating conditions, and we demonstrate the efficiency of the proposed scheme compared to a system that employs optimized sensor selection under orthogonal transmissions.

In this paper we propose user cooperation in Heterogenous cellular networks (HCNs) for combating intra-cell interference and improving downlink spectral efficiency (SE). The main idea we put forward is to employ interference cancellation at users associated to the small cell, when the macrocell transmits simultaneously with the small cell base station (causing intracell interference). This allows the decoding of the small cell transmission at several users. Then, in a subsequent time slot, a distributed space time code (DSTC) is used from these users in order to increase the reliability of the transmitted information to the final user. The proposed cooperative transmission scheme is compatible with state-of-the-art resource allocation mechanisms for HCNs. Our results indicate that as the user density is increased, this scheme can improve significantly the SE when compared to a non-cooperative system that does not exploit higher user densities, or a classic cooperative system that operates only during the interference-free time slots.

We consider a wireless sensor network (WSN) where each sensor node samples a random signal, places the digitized data in a buffer, and transmits the data to an access point (AP) through a wireless packet erasure communication link. The AP communicates the data to a processing center (PC) located in the cloud. Our objective is to maximize the delivery of raw data to the cloud for post- processing given the buffer space and communication bandwidth limitations at each sensor. For this system model, we propose an algorithm that is executed at the sensor and summarizes the miminum subset of the incoming data based on the available buffer space and rate of the end-to-end communication link. The PC located in the cloud uses a sequential minimum mean square error (MMSE) estimation algorithm that fuses the summarized and raw data to estimate the random signal. Our algorithm offers a building block for sensing applications that desire the collection of data in raw form.

Cooperation in wireless networks is embraced today both from cellular and device-to-device (D2D) communication standards. The reason is that this communication paradigm offers much-needed improvements in the spectral efficiency of wireless communication. Its foundation is the cooperation between nodes that are physically in close proximity. This means that it can fit very well in modern networks that are characterized by increased densities of both infrastructure and users. However, higher node density and higher number of opportunities for cooperation means also more interference. In this paper we investigate the potential throughput gain in a densely deployed interference-limited wireless network when the nodes cooperate. We consider a simple cooperative protocol that allows the relays to overhear the interfering signals and then they amplify and forward (AF) the received composite signal. We consider that the final destination employs maximum ratio combining (MRC) of the directly received signal and the forwarded interference. Given the previous protocol and decoding algorithm, we formulate the problem of link scheduling and cooperative interference forwarding (LSCIF) as a mixed integer non-linear program (MINLP) that is solved numerically. In the proposed problem formulation the aggregate signal-to-interference ratio (SIR) expression is decomposed into two separate SIR constraints. In practice this means that the signal that is received at a relay is allowed to be forwarded (the relay link is scheduled) even if the SIR of this particular signal is below the level that the final receiver can decode. Results are presented for the optimal solution and a polynomial time approximation algorithm for different traffic loads, number of relays, and demonstrate significant performance gains.

We consider a wireless sensor network (WSN) where each sensor node samples a random signal and transmits the result to an access point (AP)/fusion center (FC). The WSN operates under the sub-1GHz IEEE 802.11ah MAC/PHY standard and it also includes a relay node that may be used for forwarding the data to the FC. The FC collects the data from the sensors and the relay and estimates the random signal of each sensor with the linear weighted least squares (WLS) algorithm. The objective of the FC is to minimize the power consumption of the sensors subject to an MSE distortion constraint by selecting the use of the relay for the sensors that need it. We cast the power minimization problem as a mixed integer linear program (MILP). Our detailed simulation results indicate the efficacy of our scheme under a path loss channel model and randomly deployed sensor populations, while the performance gains are increased even more for denser sensor population. Since the IEEE 802.11ah standard leaves open aspects related to the relay selection process for IEEE 802.11ah-based WSNs, our system can be readily implemented in this emerging class of WSNs.

Heterogenous cellular networks (HCN) consist of macrocells and small cells that are overlaid in the same geographical area. Hence, is critical that the high power macrocell shuts off its transmissions for a fraction of the time to allow the low power small cells to transmit without interference. This is the time-domain resource partitioning (TDRP) mechanism. In this paper we investigate video communication in HCNs when TDRP is employed. More specifically we consider the problem of maximizing the average video quality of all users, by jointly optimizing the rate allocated to each specific video stream and the quality that it is streamed. The resulting mixed integer linear program (MILP) formulation is solved numerically. Simulation results indicate clearly that as the small cells and the users are increased the proposed system can improve significantly the video quality.

The rapid developments of advanced wireless communication technologies and mobile devices are boosting the uplink multimedia applications. In this paper, a transmit power aware cross-layer optimization scheme is proposed to achieve a good trade-off between the transmit power and the perceived video quality for Long Term Evolution uplink video streaming. Specifically, the video coding quantization parameter and encoding mode at the application layer, and the uplink transmit power as well as modulation and coding scheme at the physical layer are jointly adjusted in the cross-layer optimization. To further improve the perceptual video experience for the end user with limited transmission resources, unequal quality control is performed by enhancing the video quality of region of interest. Additionally, the structural similarity is adopted as the video quality measurement metric to make the optimized video properly preserve the structural information during the cross-layer optimization process. Experimental results show that significant performance improvements in terms of the transmit power reduction and the perceptual video quality are achieved for the proposed transmit power aware cross-layer optimization scheme.

In this paper we present an optimization framework that formalizes the inherent trade-off between the user perceived quality of wireless video, and the energy consumption cost of the network. The former is formulated in the context of the emerging heterogeneous cellular networks (HCN) based on LTE. We also consider users that employ dynamic adaptive streaming over HTTP (DASH). Our framework quantifies this trade-off carefully, by delving into the details of DASH, the LTE network, and the HCN architecture. The result is a complex problem that is solved in two levels. The master problem is responsible for decisions regarding the the user association and the average power they are allocated. The solution of this problem also entails a decision about the encoding rate of the DASH video segments. The previous decision is used in order to perform resource allocation at a finer level by considering the technical details of LTE that allocates resource blocks and power simultaneously. Numerical results are presented with realistic parameters for the LTE network and the video traffic.

In this paper, we present a multi-user cooperative protocol for wireless networks. Two sources transmit simultaneously their information blocks and relays employ opportunistically successive interference cancellation (SIC) in an effort to decode them. An adaptive decode/amplify-and-forward scheme is applied at the relays to the decoded blocks or their sufficient statistic if decoding fails. The main feature of the protocol is that SIC is exploited in a network since more opportunities arise for each block to be decoded as the number of used relays NRU is increased. This feature leads to benefits in terms of diversity and multiplexing gains that are proven with the help of an analytical outage model and a diversity-multiplexing tradeoff (DMT) analysis. The performance improvements are achieved without any network synchronization and coordination. In the final part of this work, the closed-form outage probability model is used by a novel approach for offline pre-selection of the NRU relays, that have the best SIC performance, from a larger number of NR nodes. The analytical results are corroborated with extensive simulations, while the protocol is compared with orthogonal and multi-user protocols reported in the literature.

In this paper we are concerned with the problem of data forwarding from a wireless body area network (WBAN) to a gateway when body shadowing affects the ability of WBAN nodes to communicate with the gateway. To solve this problem we present a new WBAN architecture that uses two communication technologies. One network is formed between on-body nodes, and is realized with capacitive body-coupled communication (BCC), while an IEEE 802.15.4 radio frequency (RF) network is used for forwarding data to the gateway. WBAN nodes that have blocked RF links due to body shadowing forward their data through the BCC link to a node that acts as a relay and has an active RF connection. For this architecture we design a network layer protocol that manages the two communication technologies and is responsible for relay selection and data forwarding. Next, we develop analytical performance models of the medium access control (MAC) protocols of the two independent communication links in order to be used for driving the decisions of the previous algorithms. Finally, the analytical models are used for further optimizing energy and delay efficiency. We test our system under different configurations first by performing simulations and next by using real RF traces.

Heterogenous cellular networks (HCN) introduce small cells within the transmission range of a macrocell. For the efficient operation of HCNs it is essential that the high-power macrocell shuts off its transmissions for an appropriate amount of time in order for the low-power small cells to transmit. This is a mechanism that allows time-domain resource partitioning (TDRP) and is critical to be optimized for maximizing the throughput of the complete HCN. In this paper, we investigate video communication in HCNs when TDRP is employed. After defining a detailed system model for video streaming in such an HCN, we consider the problem of maximizing the experienced video quality at all the users, by jointly optimizing the TDRP for the HCN, the rate allocated to each specific user, and the selected video quality transmitted to a user. The NP-hard problem is solved with a primal-dual approximation algorithm that decomposes the problem into simpler subproblems, making them amenable to fast well-known solution algorithms. Consequently , the calculated solution can be enforced in the time scale of real-life video streaming sessions. This last observation motivates the enhancement of the proposed framework to support video delivery with dynamic adaptive streaming over HTTP (DASH). Our extensive simulation results demonstrate clearly the need for our holistic approach for improving the video quality and playback performance of the video streaming users in HCNs.

The heterogeneous wireless network (HetNet) paradigm is based on the deployment of low power small cell base stations (SCBS) close to the user in parallel with a macrocell BS (MBS) that provides umbrella coverage. However, since these SCBSs are expected to be deployed in significant numbers, their increased density creates new problems but also new optimization opportunities. In this paper we design a video streaming system for HetNet configurations that allow the SCBSs to opportunistically cooperate, while their backhaul link can be either wireless or wired. The first component of our system is an algorithm that is executed at each SCBS and is responsible for opportunistically overhearing packet transmissions from the MBS and other SCBSs and forwarding them to the users. The second component of our system is a rate allocation algorithm that is executed at the video streaming server. Our system offers different performance benefits depending on the HetNet backhaul configuration. Our performance evaluation with high quality 4K videos, indicates that in the wireline backhaul case video streaming experiences lower delay, while in the wireless backhaul case our system improves the capacity that is available for video communication.

Video delivery to mobile users is one of the largest challenges that network operators face today. In this work we consider a heterogeneous cellular network with storage capable small-cell base stations, and study this problem for pre-stored video files that can be encoded with two different schemes, namely versions or layers, in various qualities. We introduce a framework for the joint derivation of video caching and routing policies for users with different quality requirements. This allows the operator to optimize a balanced objective of incurred servicing cost, and users experienced delay, according to his priorities. The numerical results indicate that versions and layers may have different impact on the delay and servicing cost, depending on the diversity of users' demand, and that the cost-delay trade off is affected by the network's load.

Interfering transmissions of independent sources is shown to provide benefits at a cost of complex receiver design. In this paper, we study the optimum transmission of correlated Gaussian sources by allowing opportunistic interference in order to minimize the expected distortion. We assume a successive interference cancellation decoder at the destination and consider a Wyner-Ziv type setup to transmit the correlated sources along with interference. We show how interference can be utilized to improve the end-to-end distortion for correlated sources.

In this paper we propose a cooperative protocol and a symbol decoding algorithm that target improved performance in the presence of inter-cell interference (ICI) in wireless cellular networks. Our scheme uses the wireless signals that interfere at relay nodes located at the cell edge instead of discarding them. The relays are non-regenerative and apply a space-time code (STC) to the interfered signal. Subsequently, the relays broadcast the coded signals. At the destinations the interfered and ST-coded signals are decoded in two stages. First by decoding the STC, and then by applying successive interference cancellation (SIC). Simulation results for Rayleigh fading channel reveal significant throughput benefits even in the low SNR regime. We also present results for a small-scale LTE-based cellular scenario and we compare our scheme against the Coordinated MultiPoint (CoMP) transmission mode of LTE-A that is based on beamforming.

In this paper we consider packetized video transmission in cooperative relay-based wireless networks. We propose algorithms for optimized relay selection that take into account the content of each specific video packet. Our first algorithm, that is designed for a narrowband flat-fading channel and single-carrier modulation, selects jointly the optimal relay and video packet for forwarding. Our next algorithm is an extension of our main idea for OFDM modulation that is suitable for frequency-selective fading channels. In this case in addition to optimized packet and relay selection, our algorithm also jointly selects the optimal power level for each subcarrier. The key benefit of our algorithms is that they are fully distributed since they require no explicit communication among the relays but they only use passively collected information. We perform an extensive evaluation of our algorithms for different system configurations.

In this paper we propose a new cooperative packet transmission scheme that allows independent sources to superimpose over-the-air their packet transmissions. Relay nodes are used and cooperative diversity is combined with distributed space-time block coding (STBC). With the proposed scheme the participating relays create a ST code for the over-the-air superimposed symbols that are received locally and without proceeding to any decoding step beforehand. The advantage of the proposed scheme is that communication is completed in fewer transmission slots because of the concurrent packet transmissions, while the diversity benefit from the use of the STBC results in higher decoding performance. The proposed scheme does not depend on the STBC that is applied at the relays. Simulation results reveal significant throughput benefits even in the low SNR regime.

In this paper we present a multiple access scheme for wireless networks that allows multiple nodes to broadcast their packets simultaneously. The simultaneously broadcasted packets are superimposed with a random pattern at each network node that receives them. A subset of the network nodes act as cooperative relays and forward the locally superimposed packets without decoding them. Each destination node recovers the desired packet with a successive interference cancelation (SIC) and decoding algorithm that exploits the pattern of the superimposed transmissions. Different flavors of the basic scheme are investigated and combine amplify-and-forward (AF) from the relays, and also retransmissions from the initial sources.

In this paper, we present an implementation design of a TDMA protocol for the canonical diamond-topology network containing a source, two relays and a destination (single unicast session). Getting inspired by the established Lyapunov-methodology, we propose an online strategy for the relay selection/scheduling problem. In contrast to existing works, we implement this strategy inside the proposed TDMA protocol in order to operate over a CSMA enabled Wi-Fi infrastructure-less network. We elaborate a network controller within the TDMA frame to solve a global optimization problem at each time slot in a centralized manner. In our formulation, we consider the class of scheduling policies that select concurrently a non-interfering subset of links. Our architecture is tailored to achieve the objectives of stabilizing the network and either maximizing throughput or minimizing the total power consumption. Our scheme has been implemented and tested thoroughly through experimentation in the NITOS wireless testbed by exploiting Wi-Fi technology features. The results revealed significant increase in networking efficiency for throughput maximization.

We consider the problem of modeling the transmission of real-time data from a single node of a wireless sensor network to the next hop or access point. Generated packets are placed in a single position buffer and are transmitted over the wireless medium to the next hop, or they are discarded and replaced when a new data packet is generated from the sensor. To increase the percentage of packets delivered on-time to the next hop, we introduce a thresholding policy that is supported by an analytical model and it is responsible for deciding whether to transmit a data packet or drop it and transmit the next one. Our model considers the behavior at single sensor where data are generated with a Poisson process and have a fixed deadline associated to them while the impact of the other sensors is modeled through their effect on the MAC service time. However, we also evaluate the performance of the packet dropping scheme in a multi-hop sensor chain. The proposed policy leads to a higher percentage of on-time delivered packets in both scenarios.

In this paper we present a cooperative medium access control (MAC) protocol that is designed for a physical layer that can decode interfering transmissions in distributed wireless networks. The proposed protocol pro-actively enforces two independent packet transmissions to interfere in a controlled and cooperative manner with the help of a relay. To enable distributed, uncoordinated, and adaptive operation of the protocol, a relay selection mechanism is introduced so that the optimal relay is selected dynamically and depending on the channel conditions. The most important advantage of the protocol is that interfering transmissions can originate from completely independent unicast transmissions from two senders. We present simulation results that validate the efficacy of our proposed scheme in terms of throughput and delay.

In this paper, we present a video streaming system for wireless networks that employs utility optimization of pre-compressed video at the application layer, together with a novel cooperative wireless physical layer (PHY) that allows interfering transmissions. Our system model considers multiple and independent unicast or multicast transmissions between network nodes while a number of them serve as relays. For this new PHY the average transmission rate that each sender-destination pair can achieve is estimated first. Next, we show that the utility optimization problem can be simplified due to the features of the proposed PHY. Subsequently, we devise a utility optimization algorithm that is executed independently at each sender and it derives the optimal video packets that should be sent in the wireless link given the calculated rate constraint. Simulation results demonstrate that for unicast video streaming scenarios and with the utility optimization framework, allowing packets to interfere is a better choice than employing a typical cooperative PHY. For the case of multicast video delivery the performance of the proposed scheme is significantly improved for good channel conditions while the improvement is minimized in the low channel SNR regime when compared to the unicast scenario.

Body-coupled communication (BCC) is a technology that is based on the use of capacitively-coupled electric fields over the human body. In this paper we highlight the design and implementation of a medium access control (MAC) protocol specifically designed to meet the requirements of BCC-enabled body area networks (BANs). We propose a set of specific protocol enhancements over well-known MAC protocols that are based on the concept of low-power listening. We present the proposed protocol in conjunction with the embedded software implementation for the prototype hardware platform. The purpose of this approach is to demonstrate that several protocol enhancements were motivated by observations on the actual BCC hardware and its intricacies. Finally, we present a series of experiments under real conditions.

In this paper we present a cooperative medium access control (MAC) protocol that is designed for a physical layer that can decode interfering transmissions in distributed wireless networks. The proposed protocol pro-actively enforces two independent packet transmissions to interfere in a controlled and cooperative manner. The protocol ensures that when a node desires to transmit a unicast packet, regardless of the destination, it coordinates with minimal overhead with relay nodes in order to concurrently transmit over the wireless channel with a third node. The relay is responsible for allowing packets from the two selected nodes to interfere only when the desired packets can be decoded at the appropriate destinations and increase the sum-rate of the cooperative transmission. In case this is not feasible, classic cooperative or direct transmission is adopted. To enable distributed, uncoordinated, and adaptive operation of the protocol, a relay selection mechanism is introduced so that the optimal relay is selected dynamically and depending on the channel conditions. The most important advantage of the protocol is that interfering transmissions can originate from completely independent unicast transmissions from two senders. We present simulation results that validate the efficacy of our proposed scheme in terms of throughput and delay.

In a number of application domains, the volatility of the environment where Wireless Sensor Networks (WSNs) should operate engenders time constraints on the generation, processing, and communication of sensory data. In this paper, we investigate the use of network coding and service reneging for the timely delivery of messages between wireless sensor nodes that utilize the IEEE 802.15.4 MAC/PHY standard. More specifically, we study the real-time performance of algebraic coding and traffic regulation under different traffic loads and for different timeliness requirements. Our simulation results indicate that both techniques can be on their own effective measures to improve on-time delivery of messages in overload conditions. An additional gain in performance is obtained when coding is carefully coupled with a traffic regulation based on proactive cleaning of transmission queues.

The concept of analog network coding (ANC) considers the concurrent transmission of signals over the wireless medium so that they intentionally interfere. Higher network throughput can be achieved with ANC when an intended transmission that a receiver desires to receive is made to interfere with a concurrent transmission of a signal known a priori at the receiver. In this paper, we present a cooperative protocol that exploits ANC. We consider a system with cooperative relaying of overlapped transmissions from two independent users. More specifically, we explore the case that network nodes may allow the transmitted signals to interfere both at the final destination node, and also at an intermediate node that acts subsequently as a relay. The relay employs a cooperative and multicast amplify-and-forward protocol so that the two destinations can use the interfered signals in order to recover the desired packet. We analyze this protocol in terms of the achieved rate and present an algorithm to recover signals from the overlapped transmissions. We study the impact of overlapping in signal transmissions on the throughput of the system. We show that even with considerable overlapping, the throughput under the proposed protocol is better in comparison to an orthogonal amplify-and-forward protocol.

The 14 papers in this special issue are divided into four categories: theoretical frameworks and schemes; integration of media processing and network protocols; heterogeneity; and quality evaluation.

Real-time video communication over wireless multihop networks has gained significant interest in the last few years. In this paper, we focus our attentions on the problem of source coding and link adaptation for packetized video streaming in wireless multihop networks when network nodes are media-aware. We consider a system where source coding is employed at the video encoder by selecting the encoding mode of each individual macro-block, while error control is exercised through application-layer retransmissions at each media-aware network node. For this system model, the contribution of each communication link on the end-to-end video distortion is considered separately in order to achieve globally optimal source coding and ARQ error control. To reach the globally optimal solution, we formulate the problem of joint source and distributed error control (JSDEC) and devise a low-complexity solution algorithm based on dynamic programming. Extensive experiments have been carried out on the basis of H.264/AVC codec to demonstrate the effectiveness of the proposed algorithm over the existing joint source and channel coding (JSCC) algorithm in terms of PSNR perceived at the decoder under time-varying multihop wireless links.

Network coding has been shown to be a very promising technique for increasing the throughput in wireless ad hoc networks. In this paper, we set to define simple extensions for distributed medium access control (MAC) protocols that optimize the transmission of network-coded packets independently of the actual network coding algorithm. The main characteristic of the proposed protocol is that it improves the efficiency of coding decisions and allows verifying the decodability of packets before they are transmitted. The aforementioned goals are achieved first by adopting minor extensions to the channel access scheme, and second by introducing a new algorithm that manages intelligently the data packets that are stored at the MAC queue of each network node. The later algorithm improves the knowledge of a node regarding the available correct coding opportunities by the use of opportunistic acknowledgments, and by maintaining virtual buffers for the overheard data packets. For the proposed protocol, we also develop an analytical performance model that is used for evaluating its performance in conjunction with simulations. Extensive simulations are presented for several ad hoc network topologies that test all the features of the proposed protocol. Significant throughput improvement can be observed when our protocol is compared with network coding schemes that do not exploit the full potential of opportunistic listening.

In this paper, we present a set of simple extensions to the IEEE 802.11 MAC that allow the efficient realization of wireless network coding. The main characteristic of the proposed protocol is that it improves the efficiency of packet coding decisions and allows verifying the decodability of packets before they are transmitted over the network. The previous goals are achieved by adopting two techniques. First, we introduce the concept of opportunistic acknowledgments (OACKs), and second, we adopt the use of virtual buffers that are used for selectively storing overheard packets by a particular node. We performed extensive simulations for several ad hoc network topologies that test the proposed protocol. Our results indicate that significant throughput enhancements can be achieved when compared with network coding schemes that are realized with the legacy 802.11 MAC.

We consider the problem of rate-distortion (RD) optimized media streaming in unstructured peer-to-peer (P2P) overlay networks. We formulate the aforementioned problem as a distributed rate allocation problem, and we solve it by applying classical decomposition techniques so that the network-wide utility of the media distortion is minimized. Information exchange between the peers is employed to ensure updates on the price of the locally calculated rate allocation. Media packets are also piggybacked with RD preambles that contain information regarding their impact on the decoder distortion and their size. The benefit of the aforementioned approach is that peers can convert the calculated optimal rate allocation into simple forwarding or dropping actions allowing thus a lightweight implementation. Our simulation results indicate that significant quality benefits can be achieved when the precise RD characteristics of a media description are taken into account by the streaming algorithm.

TCP Friendly Rate Control (TFRC) has been designed to provide smoother sending rate than TCP for multimedia applications. However, most existing work on TFRC is restricted within exploring the performance of TFRC itself in wired or wireless networks without considering the interaction between TFRC and other network layers. This paper proposes a quality- driven TFRC framework for real-time video streaming, where real-time video coding at the application layer and the packet sending rate at the transport layer are jointly optimized. The proposed framework is formulated to find the optimal video coding parameters and the sending rate to minimize the end- to-end expected video distortion under a given video playback delay constraint. The proposed framework has been implemented and tested by using both H.264/AVC codec and NS-2 simulator. Experimental results demonstrate that the proposed joint optimization framework can significantly improve the received video quality over the existing schemes, especially when delay bound is tight.

In this paper, we present a cross-layer methodology for video streaming in wireless LANs that employs joint source coding and rate adaptation at the wireless physical layer (PHY). More specifically, we investigate the impact of adapting the PHY transmission rate, and thus changing the throughput and packet loss channel characteristics, on the rate-distortion (RD) performance of a transmitted video sequence. We develop an algorithm that jointly optimizes source coding, application-layer channel coding, and the PHY rate adaptation. Simulation results obtained with the H.264/AVC codec, demonstrate video quality improvements when compared with a system that employs PHY rate adaptation independently from the application layer.

In this paper, we present a cross-layer approach for video transmission in wireless LANs that employs joint source and application-layer channel coding, together with rate adaptation at the wireless physical layer (PHY). While the purpose of adopting PHY rate adaptation in modern wireless LANs like the IEEE 802.11a/b is to maximize the throughput, in this paper we exploit this feature to increase the robustness of wireless video. More specifically, we investigate the impact of adapting the PHY transmission rate, thus changing the throughput and packet loss channel characteristics, on the rate-distortion performance of a transmitted video sequence. To evaluate the video quality at the decoder, we develop a cross-layer modeling framework that considers jointly the effect of application-layer joint source-channel coding (JSCC), error concealment, and the PHY transmission rate. The resulting models are used by an optimization algorithm that calculates the optimal JSCC allocation for each video frame, and PHY transmission rate for each outgoing transport packet. The comprehensive simulation results obtained with the H.264/AVC codec demonstrate considerable increase in the PSNR of the decoded video when compared with a system that employs separately JSCC and PHY rate adaptation. Furthermore, our performance analysis indicates that the optimal PHY transmission rate calculated by the proposed algorithm, can be significantly different when compared with rate adaptation algorithms that target throughput improvement.

In this paper, we propose a cross-layer error control framework for robust and low delay multimedia streaming in tandem-connected IEEE 802.11 wireless LANs and the Internet. For this network configuration, we model the end-to-end delay and packet loss rate as a function of the automatic repeat request (ARQ) and forward error correction (FEC) error control mechanisms that are employed at the application and wireless link layers. The analytical model is used as the basis of a delay-constrained error control algorithm that adapts the protection level at the application and link layers so that the end-to-end packet loss rate is minimized. With extensive simulations, we validate the efficiency of the proposed cross-layer error control methodology for delay-sensitive pre-compressed video streaming.

In this paper, we focus on improving the robustness of packetized multimedia streaming in tandem-connected wireless LANs and wireline packet switched networks. To this aim we initially develop an analytical model that expresses the end-to-end packet loss rate and latency, as a function of the retransmission-based error control mechanisms employed both at the application and wireless link layers. The developed model is the basis of an algorithm that dynamically identifies the optimal number of retransmissions at each protocol layer, so that the overall effective packet loss rate is minimized. Realistic video streaming experiments show considerable quality improvements in terms of PSNR, by avoiding the overall number of retransmissions.

This paper introduces a new idea for implementing playback adaptation algorithms suitable for video streaming applications in wireless IP networks. The proposed algorithm depends on the accurate estimation of the expected latency of lost video packets. The latency is estimated according to the transport protocol being used, which in our case is TCP. The client uses this estimate in order to adjust the playback rate according to a new algorithm so that both buffer underflows and overflows can be avoided. The proposed algorithm in non-intrusive to the protocol stack since it does not require any modifications to the transport protocol. To validate the basic principles behind our idea, we provide a set of comparative experimental results with other streaming systems in terms of the rate of underflow events and the actual PSNR.

At the forefront of the recent advances in mobile networks is the development of sophisticated mobility management mechanisms that are usually based on Mobile IP and its derivatives. Based on these mobility management protocols, several studies that characterize transport protocol performance have been presented. In this work we move one step further, and present a joint performance evaluation model of TCP and TFRC, with the underlying IP-based mobility protocols. We develop stochastic models that can characterize the protocol performance during handoffs between heterogeneous wireless networks like WLAN, cellular, or WMAN. We present performance evaluation results for validating the developed models under a set of different handoff scenarios. The developed model can be utilized as a basis for further analytical evaluation of new mobility management protocols, allowing thus a fast and accurate comparison.

In this paper we propose a playback adaptation algorithm for video streaming with TCP in wireless networks where both handoffs and random wireless errors are possible. Primary task of the algorithm is the estimation of the expected latency for the delivery of the lost packets by TCP. After this procedure is performed, the client adjusts the playback rate according to our algorithm so that both buffer underflows and overflows can be avoided. The proposed algorithm in non-intrusive to the protocol stack since it does not require any modifications to the baseline TCP protocol. To validate the basic principles behind our idea, we provide a set of comparative experimental results with other TCP-based streaming systems in terms of the rate of underflow events and the PSNR

In this paper we explore use of a new rate-distortion metric for optimizing real-time Internet video streaming with the transmission control protocol (TCP). The basic idea is to combat packet delays caused by TCP retransmissions that are essentially interpreted as errors by the streaming application. To this aim, we develop an analytical model of the expected video distortion at the decoder with respect to the expected latency for TCP packets, the channel state, and the error concealment method at the receiver. This metric is exploited with the design of a new algorithm for rate-distortion optimized encoding mode selection for video streaming with TCP (ROOMS-TCP). Real-time video streaming experiments show considerable improvement in PSNR in the range of 2 db over currently proposed TCP-based streaming mechanisms

Abstract At the forefront of the recent advances in mobile networks, is the development of sophisticated mobility management mechanisms that are usually based on Mobile IP and its derivatives. Based on these mobility management protocols, several studies that characterize transport protocol performance have been presented. In this work we move one step further, and present a joint performance evaluation model of TCP and TFRC, with the underlying IP‐based mobility protocols. We develop stochastic models that can characterize the protocol performance during handoffs between heterogeneous wireless networks like WLAN, cellular, or WMAN. We present performance evaluation results for validating the developed models under a set of different handoff scenarios. The developed model can be utilized as a basis for further analytical evaluation of new mobility management protocols, allowing thus a fast and accurate comparison. Copyright © 2006 John Wiley & Sons, Ltd.

We propose a novel end-to-end architecture for streaming H.264/AVC (advanced video coding) unicast video over the Internet. The proposed video streaming architecture is based primarily on a new transport layer protocol, the stream control transmission protocol (SCTP). We show that the network-friendly specification of H.264/AVC and the novel technical characteristics of SCTP, when coupled together are able to provide a highly adaptive and flexible system for unicast video streaming. With simulation results we prove (1) that our system is capable of maintaining good perceptual quality under various loss conditions and (2) that the proposed system maintains TCP-friendliness.

The advent of wireless ad-hoc networks has provided a cost effective way for exploiting mobile hosts in the absence of infrastructure. However, the full potential that these networks offer for performance enhancement still remains to be exploited. In this paper we exploit the existence of multiple paths between mobile hosts in an ad-hoc network. More specifically, we introduce a new scheme for load-balancing for mobile multihomed hosts that are part of an ad-hoc network. Our approach differs from previously proposed schemes since it consists an entirely end-to-end approach built on top of the stream control transmission protocol (SCTP), while the dynamic source routing (DSR) protocol is used for routing. We provide simulation results that prove the efficiency of our approach based on realistic mobility scenarios.

We present a number of modifications to the stream control transmission protocol (SCTP) recently adopted by IETF; they allow bandwidth aggregation over the multiple interfaces of a host. We show that it is possible to implement a number of algorithms for bandwidth aggregation, with only a small number of modifications to the basic SCTP. Our simulation results clearly depict the efficiency of our approach in terms of bandwidth utilization. Furthermore, we implement and evaluate a mechanism for identifying bottlenecks that are shared by flows from the same aggregate connection. Our purpose is to show that SCTP is a good candidate for building a practical protocol for bandwidth aggregation that is fair and supportive of TCP.

In this paper we evaluate the performance of the stream control transmission protocol (SCTP) in a wireless ad-hoc network environment under two routing protocols: DSR and AODV. Furthermore, we propose a cross-layer optimization to the SCTP and DSR protocols that handle pro-actively route failures. The novelty in the proposed mechanism consists in the identification of route failures which is done at the transport layer, allowing thus faster recovery. Our simulation results prove the efficiency of our approach for a variety of mobility scenarios.

A methodology for power optimization of the data memory hierarchy and instruction memory, is introduced. The effect of the methodology on a set of widely used multimedia application kernels, namely full search, hierarchical search, and parallel hierarchical one dimension search, is demonstrated. Three different target architecture models are used. The issues of the data memory power reduction and instruction memory are tackled separately. We find the power optimal data memory hierarchy applying the appropriate data-use transformation, while the instruction power optimization is done using suitable cache memory. Using data-reuse transformations, performance optimization techniques, and instruction-level transformations, we perform exhaustive exploration of all the possible alternatives to reach power efficient solutions. The experimental results prove the efficiency of the methodology in terms of power for all the multimedia kernels.

A comparison study of four blocking matching algorithms is presented in terms of performance, energy consumption and area, assuming five matching criteria. Two widely-used programmable processor cores, namely the embedded processor, ARM, with specialized memory hierarchy and the general purpose processor, Pentium/sup /spl copy//, are chosen for performing the comparative study, and eventually, determining the most efficient solution for certain design specifications. Adopting a systematic design methodology, efficient high-level transformations are applied to the considered algorithm structures resulting in performance-, energy-, and area-optimized solutions The plethora of alternative implementations under the different distance criteria and processor cores, offers flexibility to the designer in finding the most suitable solution for him/her, given the design specifications.