Compressive sensing (CS) makes it possible to more naturally create compact representations of data with respect to a desired data rate. Through wavelet decomposition, smooth and piecewise smooth signals can be represented as sparse and compressible coefficients. These coefficients can then be effectively compressed via the CS. Since a wavelet transform divides image information into layered blockwise wavelet coefficients over spatial and frequency domains, visual improvement can be attained by an appropriate perceptually weighted CS scheme. We introduce such a method in this paper and compare it with the conventional CS. The resulting visual CS model is shown to deliver improved visual reconstructions.

We propose the energy efficient MAC algorithm in this pa per. In the proposed algorithm, each node sets the contention window size with respect to the residual energy, the harvesting energy and the transmit power. This algorithm makes the sensor nodes consume their energy efficiently. To achieve this goal, we use the game theory and the cross-layer optimization. Introducing the non-cooperative game, we can formulate the utility function easily. In this paper, we can allocate the optimal power by the cross-layer optimization on the PHY and the MAC.

Compressive Sensing (CS) aims to recover a sparse signal from a small number of projections onto random vectors. Because of its great practical possibility, both academia and industries have made efforts to develop the CS's reconstruction performance, but most of existing works remain at the theoretical study. In this paper, we propose a new Block Compres-sive Sensing (nBCS), which has several benefits compared to the general CS methods. In particular, the nBCS can be dynamically adaptive to varying channel capacity because it conveys the good inheritance of the wavelet transform.

Cross-layer optimization for efficient multimedia communications is an important emerging issue towards providing better quality-of-service (QoS) over capacity-limited wireless channels. This paper presents a cross-layer optimization approach that operates between the application and physical layers to achieve high fidelity downlink video transmission by optimizing with respect to a quality criterion termed “visual entropy” using Lagrangian relaxation. By utilizing the natural layered structure of wavelet coding, an optimal level of power allocation is determined, which permits the throughput of visual entropy to be maximized over a multi-cell environment. A theoretical approach to optimization using the Shannon capacity and the Karush-Kuhn-Tucker (KKT) conditions is explored when coupling the application with the physical layers. Simulations show that the throughput gain for cross-layer optimization by visual entropy is increased by nearly 80% at the cell boundary as compared with peak signal-to-noise ratio (PSNR).

To achieve seamless multimedia streaming services over wireless networks, it is important to overcome inter-cell interference (ICI), particularly in cell border regions. In this regard scalable video coding (SVC) has been actively studied due to its advantage of channel adaptation. We explore an optimal solution for maximizing the expected visual entropy over an orthogonal frequency division multiplexing (OFDM)-based broadband network from the perspective of cross-layer optimization. An optimization problem is parameterized by a set of source and channel parameters that are acquired along the user location over a multicell environment. A suboptimal solution is suggested using a greedy algorithm that allocates the radio resources to the scalable bitstreams as a function of their visual importance. The simulation results show that the greedy algorithm effectively resists ICI in the cell border region, while conventional nonscalable coding suffers severely because of ICI.

A recent dynamic increase in demand for wireless multimedia services has greatly accelerated the research on dynamic channel adaptation of high quality video applications. In this paper, we explore a theoretical approach to cross-layer optimization between multimedia and wireless networks by means of a quality criterion termed ldquovisual throughputrdquo for downlink video transmission using a layered coding algorithm. We obtain the optimal loading ratio of orthogonal frequency division multiple access (OFDMA) subcarriers through an optimization problem balancing the tradeoff relationship between inter-cell interference (ICI) and channel throughput. Through numerical link capacity analysis, we show that the upper bounds of the visual throughput gain at the cell boundary is obtained at about 27%.

Explosively increasing demands for the wireless multimedia data have accelerated to research for adapting higher quality video applications in extensive efforts. In this paper, we explore a theoretical approach to cross-layer optimization between multimedia and wireless network by means of a quality criterion termed "visual entropy" for downlink video transmission, using a layered coding algorithm. We obtain the optimal loading ratio through an optimization problem, which aims at balancing the trade-off relationship between ICI (Inter-Cell Interference) and channel throughput. In the simulation, we show that the throughput gain in terms of visual entropy at the cell boundary is increased by up to about 32%.

For seamless multimedia streaming services, it is very important to overcome severe ICI (intercell interference) over wireless networks, particularly in the cell border region. SVC (scalable video coding) has been actively studied due to its advantage of channel adaptation. In this paper, we study an optimal solution for maximizing the expected visual entropy over an OFDM (orthogonal frequency division multiplexing)-based broadband network from the aspect of cross layer optimization. Based on this approach, an optimal set of source and channel parameters is obtained according to the user location over a multi-cell environment. From the numerical results, we quantify the optimal visual gain attained from the single layer video coding or the SVC according to channel capacity v.s. source data rate.

Various approaches for analyzing handover have been developed to guarantee the QoS (Quality of Service) of multimedia services over mobile communication networks. However, no framework for multicarrier-based broadband systems, such as MC-CDMA (Multi-Carrier Code Division Multiple Access) or OFDMA (Orthogonal Frequency Division Multiple Access) is available, from the perspective of link capacity. This paper presents a handover technique, referred to as semi-soft handover utilizing macro diversity, which permits both hard and soft handover advantages for services over multicarrier-based broadband networks to be retained. A theoretical analysis is then performed to measure the handover gain over the forward link in terms of an outage probability. The simulation data verifies that the semi-soft handover outperforms other traditional handover techniques, in particular, for high data rate services.

Cross-layer optimization for efficient multimedia communications has been an emerging issue, in terms of providing better QoS (quality of service) over a capacity-limited wireless channel. This paper presents a cross-layer optimization approach between multimedia and wireless network layers for downlink image transmission by means of a quality criterion termed "visual entropy" using Lagrangian relaxation. By utilizing the natural layered structure of wavelet coding, an optimal level of power allocation is determined, to permit the throughput of visual entropy for downlink broadband wireless systems to be maximized over a multi-cell environment. In the simulation, the throughput gain of visual entropy is dramatically increased up at the cell boundary, with respect to the case of no considerations.

Wavelet image coding exhibits a robust error resilience performance by utilizing a naturally layered bitstream construction over a band-limited channel. In this letter, a new measure that appears to provide a better assessment of visual entropy for comparing and evaluating progressive image coders is defined based on a visual weight over the wavelet domain. This visual weight is characterized by the human visual system (HVS) over the frequency and spatial domains and is then utilized as a criterion for determining the coding order of wavelet coefficients, resulting in improved visual quality. A transmission gain, which is expressed by visual entropy, of up to about 23% can be obtained at a normalized channel throughput of about 0.3. In accordance with the subjective visual quality, a relatively high gain can be obtained at a low channel capacity.

Wavelet image coding exhibits a robust error resilience performance by utilizing the naturally layered bitstream construction over a band-limited channel. In this paper, a closed form of the visual entropy is defined based on a visual weight over the wavelet domain, which is characterized by the HVS (human vision system) over the frequency and spatial domains. This visual weight is then utilized as a criterion for determining the coding order of wavelet coefficients and the resulting improved visual quality is demonstrated. In terms of visual entropy, a transmission gain of up to 20 % can be obtained at a normalized channel throughput of about 0.3.