Abstract
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.
Highlights
Vehicular ad-hoc networks (VANETs) can provide multimedia communication between vehicles with the aim of providing efficient and safe transportation [1]
Video transmission has been considered as a challenging task for VANETs, because video transmission over VANETs can significantly increase bandwidth [2]
This work focuses on the development of video codec that supports real-time video transmission over VANETs for road safety applications
Summary
Vehicular ad-hoc networks (VANETs) can provide multimedia communication between vehicles with the aim of providing efficient and safe transportation [1]. Vehicles with different sensors can exchange and share information for safely breaking, localization and obstacle avoiding. The sharing of traffic accident’s live video can improve the rescue efficiency and alleviate traffic jams. Video transmission has been considered as a challenging task for VANETs, because video transmission over VANETs can significantly increase bandwidth [2]. This work focuses on the development of video codec that supports real-time video transmission over VANETs for road safety applications. The demanding challenges of VANETs are bandwidth limitations and opportunities, connectivity, mobility, and high loss rates [3]. Because of the resource-demanding nature of video data in road safety applications, bandwidth limitations is the bottleneck for real-time video transmission over
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