Low-Complexity and Hardware-Friendly H.265/HEVC Encoder for Vehicular Ad-Hoc Networks

Real-time video streaming over vehicular ad-hoc networks (VANETs) has been considered as a critical challenge for road safety applications. The purpose of this paper is to reduce the computation complexity of high efficiency video coding (HEVC) encoder for VANETs. Based on a novel spatiotemporal neighborhood set, firstly the coding tree unit depth decision algorithm is presented by controlling the depth search range. Secondly, a Bayesian classifier is used for the prediction unit decision for inter-prediction, and prior probability value is calculated by Gibbs Random Field model. Simulation results show that the overall algorithm can significantly reduce encoding time with a reasonably low loss in encoding efficiency. Compared to HEVC reference software HM16.0, the encoding time is reduced by up to 63.96%, while the Bjontegaard delta bit-rate is increased by only 0.76–0.80% on average. Moreover, the proposed HEVC encoder is low-complexity and hardware-friendly for video codecs that reside on mobile vehicles for VANETs

THE INFLUENCE OF THE PACKET SIZE ON END TO END DELAY OF VIDEO DATA CODED WITH RAPTORQ CODES AND NETWORK CODES IN VEHICULAR ADHOC NETWORKS

The transmission of video files in Vehicular Adhoc Networks (VANETs) has become very prevalent as commuters prefer video data during travel. The delay with which the data is received becomes very significant as video packets received after their scheduled deadlines become useless. The performance of the network may significantly be reduced on such packet drops especially with mobile networks. This work aims at the reduction of end to end delay of video packets by applying the two techniques- Network Coding (NC) and RaptorQ (RQ) codes. The techniques are implemented in four VANET scenarios and an extensive analysis is done by varying the packet sizes during the transmission of three files of various sizes. The End to End Delay (EED) and Packet Delivery Ratio (PDR) are measured and plotted for all scenarios. The results show the influence of packet size on these parameters considered and the suitability of the techniques applied. The observations also show that RQ proves better for smaller files and NC suits better when the file size increases

Towards video streaming in IoT environments: vehicular communication perspective

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues