Abstract
This paper proposes a power efficient multipath video packet scheduling scheme for minimum video distortion transmission (optimised Video QoS) over wireless multimedia sensor networks. The transmission of video packets over multiple paths in a wireless sensor network improves the aggregate data rate of the network and minimizes the traffic load handled by each node. However, due to the lossy behavior of the wireless channel the aggregate transmission rate cannot always support the requested video source data rate. In such cases a packet scheduling algorithm is applied that can selectively drop combinations of video packets prior to transmission to adapt the source requirements to the channel capacity. The scheduling algorithm selects the less important video packets to drop using a recursive distortion prediction model. This model predicts accurately the resulting video distortion in case of isolated errors, burst of errors and errors separated by a lag. Two scheduling algorithms are proposed in this paper. The Baseline scheme is a simplified scheduler that can only decide upon which packet can be dropped prior to transmission based on the packet's impact on the video distortion. This algorithm is compared against the Power aware packet scheduling that is an extension of the Baseline capable of estimating the power that will be consumed by each node in every available path depending on its traffic load, during the transmission. The proposed Power aware packet scheduling is able to identify the available paths connecting the video source to the receiver and schedule the packet transmission among the selected paths according to the perceived video QoS (Peak Signal to Noise Ratio--PSNR) and the energy efficiency of the participating wireless video sensor nodes, by dropping packets if necessary based on the distortion prediction model. The simulation results indicate that the proposed Power aware video packet scheduling can achieve energy efficiency in the wireless multimedia sensor network by minimizing the power dissipation across all nodes, while the perceived video quality is kept to very high levels even at extreme network conditions (many sensor nodes dropped due to power consumption and high background noise in the channel).
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