Angeliki V. Katsenou

Resource management for wireless visual sensor networks based on individual video characteristics

We propose a novel approach for the optimized network resource management of a Direct Sequence Code Division Multiple Access (DS-CDMA) visual sensor network. The visual sensors monitor different scenes of varying motion levels, thus different network resources need to be allocated to each sensor. For each recorded scene, our approach considers its individual content-related parameters, in contrast with previous methods that group the sensors according to the amount of motion present in the scene and assign the same transmission parameters to all members of a group. Cross-layer optimization is used across the physical, link and application layers. Based on quality-driven criteria (under the constraint of constant chip rate), we allocate to each node a suitable continuous power level, a discrete source coding rate and a discrete channel coding rate. The resulting problem is solved using the Particle Swarm Optimization algorithm. Experimental results demonstrate the performance and efficiency of each criterion.

Priority-based cross-layer optimization for multihop DS-CDMA Visual Sensor Networks

We propose a novel priority-based approach that enables the optimal control of the transmission power and the use of the available network resources of a multihop Direct Sequence Code Division Multiple Access (DS-CDMA) Wireless Visual Sensor Network (WVSN). TheWVSN nodes can either monitor different scenes (source nodes) or retransmit videos of other nodes (relay nodes). Moreover, in real environments the source nodes monitor different scenes that may be of dissimilar importance. Hence a higher end-to-end quality is demanded for those nodes that are assigned a higher priority. Overall, each node has different power and resource requirements, and therefore a global optimization approach is required. For the purpose of enhancing the delivered video quality of the source nodes with respect to their priorities, we define and suggest the use of priority-based optimization criteria. Experimental results that assess the proposed approach are provided and conclusions are drawn.

Motion–Related Resource Allocation in Dynamic Wireless Visual Sensor Network Environments

This paper investigates quality-driven cross-layer optimization for resource allocation in direct sequence code division multiple access wireless visual sensor networks. We consider a single-hop network topology, where each sensor transmits directly to a centralized control unit (CCU) that manages the available network resources. Our aim is to enable the CCU to jointly allocate the transmission power and source-channel coding rates for each node, under four different quality-driven criteria that take into consideration the varying motion characteristics of each recorded video. For this purpose, we studied two approaches with a different tradeoff of quality and complexity. The first one allocates the resources individually for each sensor, whereas the second clusters them according to the recorded level of motion. In order to address the dynamic nature of the recorded scenery and re-allocate the resources whenever it is dictated by the changes in the amount of motion in the scenery, we propose a mechanism based on the particle swarm optimization algorithm, combined with two restarting schemes that either exploit the previously determined resource allocation or conduct a rough estimation of it. Experimental simulations demonstrate the efficiency of the proposed approaches.

Quality-driven power control and resource allocation in wireless multi-rate Visual Sensor Networks

In the present paper, we deal with the problem of allocating the network resources in multi-rate Direct Sequence Code Division Multiple Access (DS-CDMA) Visual Sensor Networks (VSNs). We consider a single-cell system where each node uses the same chip rate, but can transmit at a different bit rate. In wireless VSNs, we face the constraints of limited power lifetime and of an error-prone environment, mainly due to attenuation and interference. The proposed cross-layer scheme enables the Centralized Control Unit (CCU) to jointly allocate the transmission power, the transmission bit rate and the source-channel coding rates for each VSN node in order to optimize the delivered video quality. The transmission power of each visual sensor assumes values from a continuous range, while the rest of the resources take values chosen from an available discrete set. The numerical results demonstrate the performance of the proposed multi-rate scheme vs a single-rate system.

Video texture analysis based on HEVC encoding statistics

In this paper, an extensive study of different video texture properties based on encoding statistics extracted from the HEVC HM reference software is presented. Mode selection, partitioning, motion vectors and bitrate allocation are among the statistics obtained from the encoder. For this study, a new dataset of homogeneous static and dynamic video textures, HomTex, is proposed. A comprehensive investigation of the results reveals a significant variability of coding statistics within dynamic textures, suggesting that this category should be further split into two relevant subcategories, continuous dynamic textures and discrete dynamic textures. This case is supported by an unsupervised learning approach on the statistics extracted. Finally, following the results obtained, some suggestions of improvements in video texture coding are presented.

Power-aware QoS enhancement in multihop DS-CDMA Visual Sensor Networks

We propose a quality-driven method for network resource allocation with transmission power control in a multihop Direct Sequence Code Division Multiple Access (DS-CDMA) Wireless Visual Sensor Network (WVSN). A multihop WVSN typically consists of source nodes that monitor different areas and relay nodes that retransmit recorded scenes. In order to achieve the best possible video quality at the receiver while consuming the least possible transmission power, we propose a joint optimization scheme that allocates the available resources among the nodes with respect to the imposed constraints. Moreover, we formulate a weighted bi-objective optimization problem and study the tradeoff between video quality and consumed transmission power. The simulation demonstrate that excessive transmission power is used when power control is omitted for a rather small quality gain for certain nodes.

On the use of clustering for resource allocation in wireless visual sensor networks

The present study is focused on the problem of quality-driven cross-layer optimization of Direct Sequence Code Division Multiple Access (DS-CDMA) Wireless Visual Sensor Networks (WVSNs). We consider a centralized topology where each sensor transmits directly to a Centralized Control Unit (CCU), which manages the network resources. In real environments, the visual sensors view and transmit scenes with varying amount of motion. Thus, each recorded video has its individual motion characteristics. Our aim is to enable the CCU to jointly allocate the transmission power and source-channel coding rates for each WVSN node under certain quality- driven criteria and constant chip rate. We consider two approaches for the cross-layer optimization scheme. In the first, the optimal set of network resources is assigned to each node according to its individual motion characteristics. In the second approach, the nodes are partitioned into clusters according to the amount of motion in the recorded scenes. Then, all nodes within a cluster are assigned identical network resources. Both approaches result in mixed-integer optimization problems, which are solved with the Particle Swarm Optimization algorithm. Experimental results demonstrate the quality/complexity trade-off for the two approaches.

Predicting video rate-distortion curves using textural features

This work addresses the problem of predicting the compression efficiency of a video codec solely from features extracted from uncompressed content. Towards this goal, we have used a database of videos of homogeneous texture and extracted both spatial and frequency domain features. The videos are encoded using High Efficiency Video Coding (HEVC) reference codec at different quantization scales and their Rate-Distortion (RD) curves are modelled using linear regression. Using the extracted features and the fitted parameters of the RD model, a Support Vector Regression Model (SVRM) is trained to learn the relationship of the textural features with the RD curves. The SVRM is tested using iterative five-fold cross-validation. The presented experimental results demonstrate that RD curve characteristics can be predicted based on the textural features of the uncompressed videos, which offers potential benefits for encoder optimization.

Understanding video texture — A basis for video compression

Encoding spatio-temporally varying textures is challenging for standardised video encoders, with significantly more bits required for textured blocks compared to non-textured blocks. It is therefore beneficial to understand video textures in terms of both their spatio-temporal characteristics and their encoding statistics in order to optimize coding modes and performance. To this end, we examine the classification of video texture based on encoder performance. For this purpose, we employ spatio-temporal features and follow a two-step feature selection process by employing unsupervised machine learning approaches across the selected feature space. Finally, supervised machine learning approaches are applied on the set of the selected features that support classification prior to encoding with up to 95.1% accuracy. The results of this study offer the potential to underpin a new informed approach to a new informed approach to codec configuration and mode selection.

Joint Quality Enhancement and Power Control for Wireless Visual Sensor Networks based on the Nash Bargaining Solution

We propose a cooperative method for resource allocation with power control in a multihop Direct Sequence Code Division Multiple Access Wireless Visual Sensor Network (WVSN). Typical multihop WVSNs consist of visual sensors that record different scenes and relay nodes that retransmit video data until the base station is reached. The error prone wireless environment contributes to the end-to-end video quality degradation. Moreover, the limited battery life span of the network nodes poses challenges on the management of power consumption. The different resource requirements of the WVSN nodes necessitate a quality-driven and power-aware resource allocation mechanism. We formulate the joint Quality Enhancement and Power Control problem based on a utility function that reflects both the benefit in terms of video quality and the cost in terms of transmission power. This function is employed by the Nash Bargaining Solution, which achieves higher fairness in terms of end-to-end video quality among all nodes. For the fairness assessment, a new metric is introduced. The experiments demonstrate the effectiveness of the proposed approach and explain the video quality-power consumption tradeoff as well as the resulting fairness-power consumption tradeoff.