Search

Due to the dynamic nature of cognitive radio networks, multi-user video streaming (with various video traffic characteristics and QoS requirements) requires efficient dynamic channel selection schemes to exploit available spectrum resources. To do this, a wireless user needs to effectively model the dynamic wireless environment and estimate the delay of video packet transmission when selecting a specific frequency channel. In this paper, we apply the priority virtual queue model for these wireless users to adapt their channel selection and maximize video qualities. The simulation results show that the proposed channel selection solution based on priority scheduling outperforms the conventional dynamic channel selection scheme by 2 dB (PSNR).

In this paper, we study the distributed spectrum allocation for autonomous users transmitting delay-sensitive information over a wireless multi-carrier network. Because there is no central moderator in the network, we propose a distributed information exchange protocol for users to exchange information, which considers their heterogeneous traffic and priorities. Based on this information exchange, we propose different prediction models that enable users to dynamically select the optimal channels for transmission. We consider two types of users - myopic and foresighted users. A myopic user selects channels passively based on the previous actions of other users, while a foresighted user applies the prediction models to select the channels in a foresighted manner. Depending on the composition of myopic users and foresighted users in the network, we define two operation modes - the homogeneous mode and the leader-follower mode. The performance of different prediction models is analyzed in each mode. Based on this analysis, we show that in the leader-follower mode, the most sophisticated model leads to the best performance. Nevertheless, in the homogeneous mode, the simplest prediction model leads to better performance. The analysis and simulation results show that in a competitive network, users should avoid adopting highly sophisticated models because this will prevent them from accurately learning each other's channel selection behavior. The proposed models and formulation can be extended to other multi-channel communications scenarios.

We consider peer-to-peer (P2P) networks, where multiple peers are interested in sharing content. While sharing resources, autonomous and self-interested peers need to make decisions on the amount of their resource reciprocation (i.e. representing their actions) such that their individual rewards are maximized. We model the resource reciprocation among the peers as a stochastic game and show how the peers can determine their optimal strategies for the actions using a Markov Decision Process (MDP) framework. The optimal strategies determined based on MDP enable the peers to make foresighted decisions about resource reciprocation, such that they can explicitly consider both their immediate as well as future expected rewards. To successfully formulate the MDP framework, we propose a novel algorithm that efficiently identifies the state transition probabilities using representative resource reciprocation models of peers. Simulation results show that the proposed approach based on the reciprocation models can effectively cope with a dynamically changing environment of P2P networks. Moreover, we show that the foresighted decisions lead to the best performance in terms of the cumulative expected rewards.

This paper addresses the problem of multi-user video transmission over the uplink of multi-carrier networks from an information theoretic perspective. Under the constraints imposed by the physical (PHY) and medium access control (MAC) layers, we exploit the unique property of state-of-the-art video coders that can provide bitstream prioritization in terms of distortion impact and solve the problem of allocating wireless resources, i.e., power and rate among multiple users such that the weighted sum of the overall video qualities is maximized. An optimality condition is derived to describe the achievable convex utility region. We start from the two-user case and develop an algorithm for the optimal resource allocation. Inspired by the intuition gained from the two-user case, we extend the algorithm to the multiple-user case. Our numerical simulations show that the proposed resource allocation algorithms give significant performance improvements as compared to application-layer agnostic solutions that do not consider the quality impact.

We propose decentralized solutions for resource negotiation, where multiple autonomous users self-organize into a coalition which shares the same network resources and negotiate the division of these resources by exchanging information about their requirements. We discuss various network resource sharing strategies that the users can deploy based on their exchanged information. Several of these strategies are designed to explicitly consider the utility (i.e., video quality) impact of multimedia applications. To quantify the utility benefit derived by exchanging different information, we define a new metric referred to as the value of information. Simulation results show the improvements that can be achieved when various information is exchanged between users, and discuss the required complexity involved in implementing the various resource negotiation strategies.

This paper considers the problem of how to allocate power among competing users sharing a frequency-selective interference channel. We model the interaction between selfish users as a non-cooperative game and analyze their strategic behavior. As opposed to the existing iterative water-filling algorithm, this paper introduces the Stackelberg equilibrium and shows the existence of this equilibrium for the investigated non-cooperative game. We model the two-user case as a bi-level programming problem and derive the necessary optimality conditions. It is analytically shown that a user can improve its performance if it knows the channel state information and the response strategy of the competing user. Due to computationally prohibitive nature of the optimal solution, a practical low- complexity approach is proposed based on the intuition gained from the necessary conditions. Numerical simulations verify the performance improvements.

In this paper, we study distributed solutions for joint power-spectrum resource allocation among delay sensitive users over multi-carrier networks. Our work differs from prior research in two ways. First, unlike prior works that only consider the impact of effective throughput of a user, our work also considers the source traffic characteristics of applications and applies queuing analysis to quantify the packet transmission delay, which is especially important to a delay sensitive user. Secondly, most prior works focus on the equilibrium of the decentralized solutions that usually require global information in a static network. Instead, we focus on interactive adaptation methods for users to dynamically maximize their expected utility based on the local observed information. We propose an interactive learning framework for users to adapt their power/channel selection to the wireless networks with interference coupling among users. Our simulation results show that the proposed interactive learning approach with information exchange among users improves the total utility significantly.

Cross-layer optimization aims at improving the performance of network users operating in a time-varying, error-prone wireless environment. However, current solutions often rely on ad-hoc optimization approaches, which ignore the different environmental dynamics experienced at various layers by a user and violate the layered network architecture of the protocol stack. This paper presents a new theoretic framework in which the cross-layer optimization problem is formulated as a layered Markov decision process (MDP). In this framework, each layer adapts its own protocol parameters and exchanges information (messages) with other layers in order to cooperatively maximize the performance of the wireless user. Hence, this layered cross-layer framework does not change the current layered architecture and is suitable for the delay-sensitive applications over wireless networks.

In this paper, we model the various wireless users streaming delay-sensitive data in a wireless network as a collection of selfish, autonomous agents that strategically interact in order to acquire the spectrum opportunities. The spectrum allocation is coordinated by a central spectrum moderator which deploys an incentive compatible mechanism. We further model the repeated spectrum competition as a stochastic game through which we are able to characterize the interaction among wireless users. Based on the observed resource allocation and corresponding rewards from previous allocations, we propose a best response learning algorithm that can be deployed by wireless users to improve the accuracy of their private information at each stage. The simulation results show that by deploying the proposed best response learning algorithm, the wireless users can significantly improve their own performance.

We propose a new complexity modeling framework for multimedia tasks. We characterize the traffic with five parameters that together we designate as a task's complexity specification (CSPEC). We extend this model to a scalable CSPEC, which can be used to characterize the many complexity- and quality-scalable operating points available to multimedia tasks. The proposed scalable CSPEC can be used by multimedia applications to match their resource requirements to available system resources.

This paper considers the problem of how to allocate power among competing users sharing a frequency-selective interference channel. We model the interaction between selfish users as a non-cooperative game. As opposed to the existing iterative water-filling algorithm that studies the myopic users, this paper studies how a foresighted user, who knows the channel state information and response strategies of its competing users, should optimize its transmission strategy. To characterize this multi-user interaction, the Stackelberg equilibrium is introduced, and the existence of this equilibrium for the investigated non-cooperative game is shown. We analyze this interaction in more detail using a simple two-user example, where the foresighted user determines its transmission strategy by solving as a bi-level program which allows him to account for the myopic user's response. It is analytically shown that a foresighted user can improve its performance, if it has the necessary information about its competitors. Since the optimal solution of Stackelberg equilibrium is computationally prohibitive, we propose a practical low-complexity approach based on Lagrangian duality theory. Numerical simulations verify the performance improvements. Possible ways to acquire the required information and to extend the formulation to more than two users are also discussed., 20 pages, 8 figures

We study the problem of optimal resource allocation for multi-user wireless video transmissions from an information- theoretic point of view. We show that the previously known optimal rate allocation solution in wireless multiaccess which maximizes the weighted sum rate is suboptimal in wireless video communications. We further derive the optimal video resource allocation by jointly considering the Application-MAC-PHY layers. This optimal scheme maximizes the weighted sum video quality of all video users for any feasible power control policy. We refer to this policy as Largest Quality Improvement Highest Possible Rate (LQIHPR). We propose a simple greedy algorithm for implementation. With the help of the inherent prioritization mechanism of video coders, we show that LQIHPR is universally optimal for all video coding schemes. Simulation results demonstrate the significant improvement LQIHPR leads to as opposed to the conventional one.

Multi-user multimedia applications such as enterprise streaming, surveillance, and gaming are recently emerging, and they are often deployed over bandwidth-constrained network infrastructures. To ensure the quality of service required by the delay-sensitive and bandwidth intensive multimedia data for these applications, efficient resource (bandwidth) management becomes paramount. We propose to deploy the well-known game theoretic concept of bargaining to allocate the bandwidth fairly and optimally among multiple collaborative users. Specifically, we consider the Nash bargaining solution (NBS) for our resource management problem. We provide interpretations for the NBS for multi-user resource allocation: the NBS can be used to maximize the system quality. The bargaining strategies and solutions are implemented in the network using a resource manager, which explicitly considers the application-specific distortion for the bandwidth allocation.

With the emergence of shared overlay network infrastructures and the recent deregularization of spectrum policies, a new, more dynamic network resource "market" is emerging. To effectively operate this new market, resource management becomes of paramount importance. This is especially important for multimedia streaming applications that require a large amount of resources to guarantee an acceptable level of multimedia quality to the end users. However, providing the necessary resources to various networked multimedia users is challenging since they have different requirements in terms of multimedia characteristics, delay, or network constraints. To simplify this problem, we propose a novel utility-based resource management scheme for multi-user multimedia transmission over networks. To manage the available resources, the resource manager deploys bargaining solutions from economics in order to explicitly consider the utility impact for different resource allocation schemes. We focus on the Kalai-Smorodinsky bargaining solution (KSBS) because it can successfully model relevant noncollaborative utility-aware fairness policies for multimedia users. The KSBS explicitly considers the application-specific utility domain (i.e., resulting multimedia quality) when performing the resource allocation. The proposed KSBS allocates the resources in such a way that the achieved utility of every participating station incurs the same quality penalty, i.e., the same decrease in video quality as opposed to their maximum achievable qualities. Our simulations show that the proposed game-theoretic resource management provides a fairer and more efficient allocation of resources in terms of derived multimedia quality.

Significant power savings can be achieved on voltage/frequency configurable platforms by dynamically adapting the frequency and voltage according to the workload (complexity). Video decoding is one of the most complex tasks performed on such systems due to its computationally demanding operations like inverse filtering, interpolation, motion compensation and entropy decoding. Dynamically adapting the frequency and voltage for video decoding is attractive due to the time-varying workload and because the utility of decoding a frame is dependent only on decoding the frame before the display deadline. Our contribution in this paper is twofold. First, we adopt a complexity model that explicitly considers the video compression specifics to accurately predict execution times. Second, based on this complexity model, we propose a dynamic voltage scaling algorithm that changes effective deadlines of frame decoding jobs. We pose our problem as a buffer-constrained optimization and show that significant improvements can be achieved over the state-of-the-art dynamic voltage scaling techniques without any performance degradation.

Based on our statistical investigation of a typical 3D (t+2D/2D+t) wavelet codec, we present a unified mathematical model to describe its rate-distortion (RD) behavior. We assess the quantization distortion of the reconstructed video frames by tracking the quantization noise along the 3D wavelet decomposition trees. The minimum bit rate to achieve this distortion is estimated by the unconditional entropy of the quantizer. Experimental results show that the model captures sequence characteristics accurately and reveals the relationship between wavelet decomposition levels and the overall RD performance.

One important aspect in peer-to-peer (P2P) networks is resource management since the available resources depend on the contributions made by the peers. In the absence of incentives, empirical data shows that a majority of the participants do not contribute resources. In order to deter such free-riders, some P2P systems impose constrains on the participating peers which allow a peer to gain benefit from the system (download content) as long as his resource contribution to it (uploaded content) exceeds a certain threshold. Under these system imposed constraints, we derive optimal upload policies for each peer given his estimated future download requirements and his previous contribution to the system. Our results show considerable improvements in the cost-benefit function by employing the optimal policy compared to heuristic upload policies. Moreover, the proposed P2P model is very general, and can be employed for investigating different applications, usage scenarios etc.

We consider a set of wireless stations that transmit and receive real-time video over a wireless channel. In our analysis, each video packet experiences a single flat fading state (block fading model) and additive Gaussian noise. We assume that the flat fading parameters are known at both transmitter and receiver sides and that each packet is sufficiently long such that, through error correction, it it possible to recover it correctly at the receiver end. Under an analogous model optimal rate allocation policies have been developed in an information theoretic framework. These policies maximize aggregate throughput or minimize queueing delays, but do not explicitly consider the application layer parameters in the rate allocation. We are interested in developing the framework to derive the optimum rate adaptation for video sources and evaluating the aggregate PSNR achievable with an optimal cross-layer design combining application-MAC-PNY layers.

Delay-constrained streaming of fully-scalable video over IEEE 802.11a/e wireless (WLANs) is of great interest for many emerging multimedia applications. In this paper, we consider the problem of video transmission over HCF1Controlled Channel Access (HCCA), which is part of the new medium access control (MAC) protocol of IEEE 802.11e. A cross-layer optimization across the MAC and application layers is used in order to exploit the features provided by the new HCCA standard, as well as by the versatility of new state-of-the-art scalable video coding algorithms. Under pre-determined delay constraints for streaming, the proposed cross-layer strategy leads to a larger number of stations being simultaneously admitted (without any loss in the video quality) than in systems that utilize application-layer only optimizations. At the same time, the fine-grain layering provided by the scalable bitstream facilitates prioritization and unequal retransmissions of packets at the MAC layer thereby enabling graceful quality degradation under channel-capacity limitations and delay constraints. The expected gains offered by the optimized solutions proposed in this paper are established through simulations.

We propose an integrated cross-layer optimization algorithm for maximizing the decoded video quality of delay-constrained streaming in a quality-of-service (QoS) enabled multi-hop wireless mesh network. The key to our algorithm is the synergistic optimization of control parameters at each node of the multi-hop network, across the protocol layers - application, network, medium access control (MAC) and physical (PHY) layers, as well as end-to-end, i.e. across the various network nodes. To drive this optimization, we assume an overlay network infrastructure, which conveys information on the conditions of each link. Quantitative results are presented that demonstrate the merits and the need for cross-layer optimization in an efficient solution for real-time video transmission using existing protocols and infrastructures.

This paper presents the design of MPEG-4 contents streaming system that provides object-based interactivity over IP networks, and shows the execution results of the proposed streaming system. The proposed streaming system deploys overlay network to support application-level multicast.

In this paper, a general quality model for scalable coded video streaming over wireless mesh networks is applied in the paper for determining the cross-layer transmission strategies over wireless multi-hop networks. Our proposed solution is enabled by the scalable coding of the video content (i. e. users can transmit and consume video at different quality layers). The adaptive cross-layer strategies - application layer packet scheduling, the policy for choosing a relay, the MAC retransmission strategies, the PHY modulation and coding schemes - are determined per packet, at each intermediate node of the network, in a distributed manner. Unlike the conventional end-to-end flow-based centralized approach, the main component of the proposed solution is a distributed dynamic routing algorithm, called self-learning policy which selects the routing relays and the corresponding time allocation for the various packets. We show that our proposed dynamic routing approach significantly outperforms the static optimization algorithm, since it provides the ability to alleviate the congestion in the changing network.

We propose a systematic technique for characterizing the workload of a video decoder at a given time and transforming the shape of the workload to optimize the utilization of a critical resource without compromising the distortion incurred in the process. We call our approach proactive resource management. We will illustrate our techniques by addressing the problem of minimizing the energy consumption during decoding a video sequence on a programmable processor that supports multiple voltages and frequencies. We evaluate two different heuristics for the underlying optimization problem that result in 50% to 92% improvements in energy savings compared to techniques that do not use dynamic adaptation

Cross protocol layer optimizations have been recently proposed for improving the performance of real-time video transmission over 802.11 WLANs. However, performing such cross-layer optimizations is difficult since the video data and channel characteristics are time-varying, and analytically deriving the relationships between quality and channel characteristics given delay and power constraints is difficult. Furthermore, these relationships are often non-linear and non-deterministic (only worst or average case values can be determined). Complex Lagrangian or multi-objective optimization problems are thus often faced. In this paper, we propose a novel framework for solving cross MAC-application layer optimization problems. More specifically, we employ classification techniques to find an optimized cross-layer strategy for wireless multimedia transmission. Our solution deploys both content- and channel-related features to select a joint application-MAC strategy from the different strategies available at the various layers. Preliminary results indicate that considerable improvements can be obtained through the proposed cross-layer techniques relying on classification as opposed to ad-hoc solutions. The improvements are especially important at high packet-loss rates (5% and higher), where deploying a judicious mixture of strategies at the various layers becomes essential.

Cross-layer optimization has been recently proposed for improving the performance of real-time video transmission over 802.11 WLANs. To perform cross-layer optimization realistic analytical models are necessary that arc able to accurately predict the interactions between layers, such that the parameters of the physical, MAC, transport and application layers are correctly identified. In this paper, we propose to modify existing IEEE 802.11 WLAN MAC models to explicitly consider how they impact and are impacted by the application layer in real-time video transmission systems. Specifically, we adapt existing MAC distributed coordination function (DCF) models to consider the actual arrival rates of video flow packets, as well as provide adaptive retransmission limit for video packets with different priorities. Conversely, the video application can adjust the maximum retransmission limit per packet and the video rate based on realistic 802.11 MAC models rather than on simplistic two-state Markov models for packet-losses. Consequently, these integrated MAC-application layer models can be used to assist the cross-layer optimization for real-time wireless transmission by realistically predicting the interactions between layers.

Many cross-layer design approaches for wireless multimedia transmission over various networks have been proposed. Truly cross-layer design solutions require joint optimization of multimedia coding, transport/network layer, medium access layer, and physical layer transmission strategies and protocols. The solution often becomes impractical because: (i) its computational complexity is high; and (ii) it needs to be recomputed as some inputs, such as the content characteristics or network conditions change. To this effect, we propose a novel framework for cross-layer design, which features: (i) the joint design strategy selection being adaptive not only to network conditions but also to multimedia content characteristics; (ii) the joint design strategy selection being based on off-line learning to address the computational complexity issue; and (iii) the off-line learning stage employing a new multiple objective optimization solution.

The robust delivery of video over emerging wireless networks poses many challenges due to the heterogeneity of access networks, the variations in streaming devices, and the expected variations in network conditions caused by interference and co-existence. By combining the continuous scalability of MPEG-4 fine-granularity-scalability (FGS) video coding with the robustness of variable modulation, a powerful method of video transmission is achieved that allows a video stream to be viewed with imperceptible degradation over a continuously changing wireless channel. In addition, sigmoid functions for throughput modelling for 802.11a networks are presented, and a game theoretic approach is suggested for further adapting the modulation and channel coding strategy to improve the video transmission performance under different wireless channel conditions.

This paper addresses the problem of efficient and robust transmission of video over a hierarchical wireless infrastructure. First, we compress the data generated by the video sources using a multi-resolution wavelet coder. Subsequently, the generated bitstreams are transmitted over a wireless infrastructure to multiple regional processing centers (RPC), which then send the distorted bitstreams to a common higher-level processing center/receiver. We propose a symmetric framework for the joint compression and error protection of the correlated video bitstreams received by the RPC. We show that significantly improved video performance can be obtained by our framework over a variety of channel conditions.

In this paper, we discuss the benefits of complexity-driven streaming and define a realistic rate-complexity-distortion framework that can assist multimedia streaming systems. We show an illustrative example for the modeling of the decoding complexity of state-of-the-art motion-compensated wavelet coding schemes. We also present a concrete system that uses these complexity metrics, which can help us quantify the performance that can be gained for multimedia streaming applications by considering complexity constraints and architectural features. Finally, we show results obtained using the proposed complexity-driven streaming architecture.

Spatio-temporal motion-compensated wavelet decomposition is an increasingly popular method for scalable video coding, with coding efficiency which is competitive with state-of-the-art nonscalable codecs. In this paper, we propose a new spatio-temporal update operator in the lifting scheme allowing efficient implementation of these temporal decompositions. We demonstrate its improved performance both theoretically, by exhibiting a decrease in the reconstruction error and by simulation results.

We discuss the benefits of using realistic rate-distortion-complexity (R-D-C) models to guide ubiquitous multimedia streaming systems. At the server or proxy, on-the-fly bitstream adaptation to varying network conditions and diverse end-devices' processing capabilities can be performed by relying on accurate (pre-computed) R-D-C models. To accommodate various types of receivers with different resources, we introduce the concept of generic complexity metrics (GCMs) which quantify in a generic manner the decoding complexity of a compressed video sequence. A state-of-the-art motion-compensated wavelet-based coding scheme is used to illustrate how R-D-C models can be computed for a particular compression scheme. Finally, we propose a receiver-driven streaming framework and show how R-D-C models can be used to assist real-time wired/wireless multimedia transmission applications.

This paper presents an efficient and robust method for joint source and channel coding over wireless fading channels. In a hierarchical wireless network, we consider cases in which a mobile transmission may be simultaneously received by multiple regional processing centers, which then collaborate by sending distorted data to a common higher-level processing center. We propose a generic framework for the joint compression and error protection of these correlated bit-streams received by distributed receivers over wireless fading channels. We show that joint decoders can achieve significant improvement.

Noise pollution in the marine environment is an emerging but serious concern. Its implications are less well understood than other global threats and largely undetectable to everyone but the specialist. In addition, the assessment of the acoustic impact of artificial sounds in the sea is not a trivial task, certainly because there is a lack of information on how the marine organisms process and analyze sounds and how relevant these sounds are for the balance and development of the populations. Further, this possible acoustic impact not only concerns the hearing systems but may also affect other sensory or systemic levels and result equally lethal for the animal concerned. If we add that the negative consequences of a short or long term exposure to artificial sounds may not be immediately observed one can understood how challenging it is to obtain objective data allowing an efficient control of the introduction of anthropogenic sound in the sea. To answer some of these questions, the choice to investigate cetaceans and their adaptation to an aquatic environment is not fortuitous. Cetaceans, because of their optimum use of sound as an ad-hoc source of energy and their almost exclusive dependence on acoustic information, represent not only the best bio-indicator of the effects of noise pollution in the marine environment, but also a source of data to improve and develop human underwater acoustic technology. Here, we present how the characteristics and performance of the sperm whale mid-range biosonar can be used to develop a mitigation solution based on passive acoustics and ambient noise imaging to prevent negative interactions with human activities by monitoring cetacean movements in areas of interest.

Advances in Internet technology and provision of constantly increasing bandwidth capabilities to end-users, have increased the need for streaming media services that can match the quality of traditional TV broadcast and offer enhanced user interactivity. In this paper, the target services, as well as specific implementation details of a scalable interactive multimedia streaming platform named ISMuS is presented. The platform is capable of providing high quality streaming service based on MPEG-4 encoded streams, through the use of scalable video coding technology and a media distribution architecture based on overlay network technology

The concepts and techniques that have dominated wired multimedia communications are not well suited for dynamic wireless environments. In recent years, research focus has been to adapt existing algorithms and protocols to the rapidly varying resources of wireless networks. For instance, packetization strategies are solely designed based on application layer information. While this design provides adequate support for multimedia applications over the Internet, it results often in poor performance in crowded wireless networks, when the interference is high or when the stations are mobile. In this paper, we show that by deploying a cross-layer aware packetization strategy, which explicitly considers the deployed compression techniques and content characteristics at the application layer as well as the protection methods at the lower layers for different channel conditions, significant improvements in multimedia performance can be obtained.

Wireless devices currently operate in a non-collaborative manner that limits their performance and the overall communication system performance, as the competing stations do not always effectively exploit the available resources. We propose to fundamentally change the non-collaborative way in which wireless stations currently interact, by allowing them to exchange information and resources to improve the performance of wireless multimedia applications. In this paper, we introduce several new fairness concepts for wireless multimedia systems that allow information and resource exchanges among stations, and show their advantages as compared to existing fairness mechanisms used in wireline communications.

Subscription differentiation can be easily supported by deploying a scalable (prioritized) bit-stream and encrypting different parts of the bit-stream with various keys. Based on the subscription level, only the keys corresponding to the authorized part of the bit-stream are transmitted to the receiver. Since state-of-the-art scalable bit-streams offer a plethora of spatial, temporal and SNR resolutions, the number of keys to communicate for the various resolutions can be very large and variable. In this paper, two methods are proposed to significantly reduce the number of keys to be communicated by using dependent keys related by one-way functions. The first method is based on using separate one-way function chains for the keys for each scalability dimension. The second method allows simultaneous key progression along multiple dimensions by using special types of quasi-commutative one-way functions called one-way accumulators. The resultant one-way structure is named the accumulator mesh.

Robust streaming of video over 802.11 wireless LANs (WLANs) poses many challenges, including coping with packet losses caused by network buffer overflow or link erasures. In this paper, a novel error protection method for wireless video, utilizing a retry-limit adaptation algorithm at the link layer, is proposed. The design of this method is motivated by the observation that the retry limit settings of the MAC layer can be optimized to minimize the overall packet losses that are caused by either link erasure or buffer overflow. To develop this method, we conducted theoretical analyses; on the relation between MAC retry-limit settings and packet error rates, and developed a real-time retry-limit adaptation algorithm to trace the optimal retry limit for the single-queue case under varying wireless channel conditions and traffic loads. Simulations show that the proposed cross-layer protection method can significantly reduce over-all packet losses and improve network throughput, resulting in better video quality.

Motion-compensated temporal filtering is an essential ingredient of recently developed wavelet-based scalable video coding schemes. The lifting implementation of these decompositions represents a versatile tool for spatio-temporal optimizations and numerous improvements have thus been proposed. An additional feature has been introduced lately by providing a 3-band lifting temporal decomposition, allowing non-dyadic scalability factors. The paper presents a motion-compensated scheme which is the 3-band equivalent of the 5/3 temporal filterbank. The proposed structure does not enter the classical lifting framework and therefore its invertibility is studied. Simulation results show higher coding efficiency, improved visual quality and more flexibility compared to the previously introduced Haar-like 2 or 3-band schemes.

Packet delay jitter and loss due to network congestion pose significant challenges for designing and deploying delay sensitive multimedia applications over the best effort packet switched networks such as the Internet. Recent studies indicate that using multiple descriptions coding (MDC) in conjunction with path or server diversity can mitigate these effects. However, the proposed MDC coding and streaming techniques are based on non-scalable coding techniques. A key disadvantages of these techniques is that they can only improve the error resilience of the transmitted video, but are not able to address two other important challenges associated with the robust transmission of video over unreliable networks: adaptation to bandwidth variations and receiving device characteristics. In this paper, we present a new paradigm, referred to as multiple description scalable coding (MDSC), that is able to address all the previously mentioned challenges by combining the advantages of scalable coding and MDC. This framework enables tradeoffs between throughput, redundancy and complexity at transmission time, unlike previous non-scalable MDC schemes. Furthermore, we also propose a novel MDSC scheme based on motion compensated temporal filtering (MCTF), denominated multiple description motion compensated temporal filtering (MD-MCTF). We use the inherent ability of current MCTF schemes, using the lifting implementation of temporal filtering. We show how tradeoffs between throughput, redundancy and complexity can easily be achieved by adaptively partitioning the video into several descriptions after MCTF. Based on our simulations using different network conditions, the proposed MD-MCTF framework outperforms existing MDC schemes over a variety of network conditions.

Motion compensated temporal filtering (MCTF) is used in wavelet video coding schemes to remove temporal redundancy. Current extensions to MCTF use bi-directional filtering and multiple reference frames to improve the coding performance significantly. However, these extensions lead to an additional cost of coding extra sets of motion vectors. In this paper we propose the use of temporal prediction to reduce the bits needed to code motion vectors. We describe several temporal prediction methods, and highlight the advantages of each scheme. We also use MV prediction during motion estimation, i.e. change the search center and the search range based on the temporal prediction, and describe the tradeoffs to be made between rate, distortion, and complexity. We perform several experiments to illustrate the gains of using temporal prediction, and identify the content dependent nature of such results.

It has been demonstrated recently that 3-D wavelet coding with motion-compensated temporal filtering (MCTF) provides a wide range of spatio-temporal-SNR scalability with state-of-the-art coding performance. However, the coding system is very different from the already standardized motion-compensated DCT (MC-DCT) video coders such as MPEG-2, MPEG-4, or H.26L. Nonetheless, the market acceptance of the new scalable technology will come much easier if backward compatibility to such previous standards would exist. In this paper, we present a new coder architecture where the base layer can be a standard MC-DCT coder, while the enhancement layer is an in-band MCTF codec operating in the overcomplete wavelet domain. First, we propose a simple extension of the scalable 3-D wavelet codec with a standard MC-DCT base layer. As a second step, to improve the performance of the proposed scalable coder over a wide range of bit-rates, we describe several small extensions to the standardized MC-DCT codec structure that can be applied for the base layer coding.

Unconstrained motion compensated temporal filtering (UMCTF) is a very general and flexible framework for temporal filtering. It allows the selection of many different filters as well as decomposition structures to allow easy adaptation to video content, bandwidth variations, complexity requirements, and in conjunction with embedded coding can provide spatio-temporal-SNR scalability. In this paper we demonstrate the content-adaptive filter selection provided within the UMCTF framework. We show improvements in coding efficiency as well as in decoded visual quality using content-adaptive filters, at different granularities.

The paper presents a layered streaming architecture for multicasting of scalable video to devices connected at various bandwidths and having different complexity-levels. The approach combines layered coding with a novel multi-channel streaming system that enables receiver-driven adaptation based on the experienced network conditions and device complexity.

As lithographic technology drives the integrated-circuit feature size towards 0.1 micron and below, overlay and alignment tolerances are becoming increasingly severe. State-of-the-art aerial imaging overlay metrology systems are limited in accuracy due to inherent limitation on tool imaging resolution and aberrations inherent to the optical system. These in effect result in measurement inaccuracy exceeding industry requirements for next generation overlay tolerances. In this paper, a spectroscopic, diffraction based, technique is proposed as an alternative solution for overlay metrology and alignment measurement in sub 0.1 micron node. With one diffraction grating on the surface overlaying a second diffraction grating on a reference layer, the spectroscopic reflection is modulated by the relative position of the two gratings. Thus, the alignment error can be extracted from broadband diffraction efficiency of measurement pads constituent to the alignment target. This novel diffraction-based overlay metrology has inherent advantages over the traditional image-based overlay metrology: the targets are less sensitive to process variations, and spectroscopic diffraction measurements are less affected by the inherent aberration of the optical system. For these reasons, a diffraction-based spectroscopic metrology has higher potential to generate overlay data that is directly related to registration errors due to alignment and not due to the process or tool aberrations. As a consequence, this technology has a higher potential to generate accurate data to correct registration errors during the lithographic process. Feasibility and accuracy of the technique is studied through a set of experiments and of rigorous simulations on grating targets with various line-width and line-to-space ratios. Correlation to aerial imaging overlay measurements is demonstrated. The results suggest the technique is capable of achieving or exceeding overlay and alignment control accuracy requirements for sub 0.1 micron design rules.

To enable video transmission over heterogeneous wireless networks, a highly scalable compression and streaming framework that can adapt to large and rapid bandwidth variations in real-time is necessary. MPEG-4 fine grained scalability (FGS) provides fine-grained SNR and temporal scalabilities, but these scalabilities are implemented and performed independently, thereby neglecting the gains that can be made from making joint SNR-temporal decisions to maximize quality. A novel fine grained SNR-temporal scalability framework called FGS+ that provides a new level of performance by considering SNR and temporal scalability jointly is presented. This new solution uses the results of our subjective tests, which indicate the levels to which SNR-quality needs to be enhanced before motion-smoothness should be improved. The study also reveals that these SNR-temporal tradeoff points vary among videos, and depend on the characteristics of the video. Based on these observations, our solution uses reference frames, enhanced relative to FGS, for prediction, improving visual quality over MPEG-4 FGS by up to 1.5 dB.

This paper presents a systematic approach to establish the complexity scalability of MPEG-4 Fine-Granular-Scalability (FGS) to fulfill the requirements of Universal Multimedia Access (UMA). We also present receiver-driven streaming strategies using the proposed FGS-based system.

MPEG-4 and H.26L have reached or neared 'international standard' status and applications are being developed based on these standards. The new H.26L standard has been proven to be an attractive choice for videostreaming over MPEG-4. However, just as in single layer MPEG-4, it lacks adaptability to bandwidth variations. This paper examines FGS-like systems for H.26L.

The universal multimedia access (UMA) paradigm is aimed at enabling multimedia data to be accessed by a large number of users/clients at any time, and from anywhere, based on their preferences, communication channel characteristics and device capabilities. For the implementation of the UMA framework, "universal" scalable video coding techniques that enable adaptive video transmission based on these three parameters are essential. Fine-granular-scalability (FGS) has recently been adopted as the MPEG-4 standard for the efficient and flexible distribution of multimedia over heterogeneous wired and wireless networks. While it has already been proven that FGS fulfils the first two UMA requirements (adaptive and robust transmission based on the channel characteristics and user preferences) due to its bit-rate scalability, the ability of FGS to provide device-based complexity-scalability was hitherto unproven. This paper proposes using an information theoretic framework, based on rate-distortion-complexity theory, for evaluating bit-rate and complexity scalable coding techniques for UMA. In this context, the ability of FGS to provide bit-rate and complexity scalability that allows easy and smooth navigation across the operational rate-distortion-complexity surface is demonstrated. Moreover, a comparison with non-scalable MPEG schemes is also given. Furthermore, we illustrate how this framework can be used in an FGS-based resource-driven video streaming system.

Fine-granularity-scalability (FGS) has recently been standardized in MPEG-4 due to its flexibility in adapting in real-time to Internet bandwidth variations and its resilience to packet-losses. However, the flexibility and robustness come at the expense of degrading video quality when compared with non-scalable MPEG-4 video coding at a given bit-rate. To reduce this visual quality penalty at low and medium bit-rates, the "Frequency Weighting" (FW) method has been standardized that allows the prioritized transmission of "low frequency" DCT coefficients. In this paper, we propose a novel scene-characteristic-dependent adaptive FW method aimed at improving the visual quality of FGS. After a thorough analysis of the FGS (i.e., SNR) residual signal at various bit-rates, we conclude that for an improved subjective quality, different FW matrices should be used to improve the FGS visual quality depending on the video sequence characteristics. Subsequently, a simple classification mechanism is developed that categorizes the video sequences based on their brightness, motion and texture activity in four distinct classes, each using a different FW matrix. For each class, the appropriate FW matrix was determined a priori based on the differences of the residual signals for two representative single-layer bit-rates. This adaptive FW (AFW) method has been subjectively evaluated and shows a clear improvement in visual quality compared with non-frequency weighted or non-adaptive frequency weighted sequences.