Driven by the quality of experience (QoE) requirement of video streaming applications in the smart city, smart education, immersive service, and connected vehicle scenarios, the existing network poses significant challenges, including ultra-high bandwidth, ultra-large storage, and ultra-low latency requirements, etc. Multi-access edge computing (MEC) is a potential technology, which can provide computation-intensive and caching-intensive services for video streaming applications to satisfy the requirement of QoE. Thus, focusing on video streaming schemes, a comprehensive summary of the state of the art applying MEC to video streaming is surveyed. Firstly, the related overview and background knowledge are reviewed. Secondly, resource allocation issues have been discussed. Thirdly, the enabling technologies for video streaming are summarized by taking account of caching, computing, and networking. Then, a taxonomy of MEC enabled video streaming applications is classified. Finally, challenges and future research directions are given.

Video surveillance in intelligent transportation systems (ITSs) is in the rapid growth stage, where video analytics is a potential technology to improve the safety of the Internet of Autonomous Vehicles (IoAV). However, massive video data transmission and computation-intensive video analytics bring an overwhelming burden for vehicular networks. Moreover, owing to the unstable network connection, the video data are not always reliable, which makes data sharing a lack of security and scalability in IoAV. In this work, we first propose a video analytics framework, where the multiaccess edge computing (MEC) and blockchain technologies are integrated into IoAV to optimize the transaction throughput of the blockchain system as well as reducing the latency of the MEC system. Furthermore, based on deep reinforcement learning, the joint optimization problem is modeled as a Markov decision process (MDP), and the asynchronous advantage actor–critic (A3C) algorithm is adopted to solve this problem. Simulation results demonstrate that our approach can fast converge and significantly improve the performance of blockchain-enabled IoAV with MEC.

In intelligent transportation systems (ITS), the vehicular ad-hoc network (VANET) is an enabling technology that can provide information exchange services among connected and autonomous vehicles (CAVs). Video streaming over VANETs is a potential application to ensure the safety of drivers and passengers and improve infotainment services. However, owing to the dynamic network topology, video transmission in VANETs is very challenging in terms of latency, reliability, and security. Therefore, a comprehensive summary of the state-of-art video streaming over VANETs is surveyed in this work. Firstly, related works and background knowledge are introduced. Then, a systematic survey on resource allocation (RA) scheme for video streaming in VANETs is provided, and some prevailing and feasible optimization tools are elaborated. Furthermore, enabling technologies of video streaming over VANETs are summarized with a special focus on the integration of video communication, caching, and computing. Finally, we give some challenges and future research directions.

Object detection for autonomous driving is a huge challenge in the cross-domain adaptation scenario, especially for the time-and resource-consuming task. Distributed deep learning (DDL) has demonstrated a considerably good balance between efficiency and computation complexity. However, the reliability of distributed deep learning is low. Moreover, the cost of training data and model is not priced well. In this work, a novel blockchain-enabled model sharing approach is proposed to improve the performance of object detection with cross-domain adaptation for autonomous driving systems. Based on blockchain and mobile edge computing (MEC) technology, a domain-adaptive you only look once (YOLOv2) model is trained across nodes, which can reduce significantly the domain discrepancy for different object categories. Furthermore, smart contracts are developed to perform data storage and model sharing tasks efficiently. The reliability of model sharing is ensured with blockchain consensus. We evaluate the proposed method under public datasets. The simulation results demonstrate that the efficiency and reliability of the proposed approach are better than the reference model.

Perceptual video coding (PVC) can provide a lower bitrate with the same visual quality compared with traditional H.265/high efficiency video coding (HEVC). In this work, a novel H.265/HEVC-compliant PVC framework is proposed based on the video saliency model. Firstly, both an effective and efficient spatiotemporal saliency model is used to generate a video saliency map. Secondly, a perceptual coding scheme is developed based on the saliency map. A saliency-based quantization control algorithm is proposed to reduce the bitrate. Finally, the simulation results demonstrate that the proposed perceptual coding scheme shows its superiority in objective and subjective tests, achieving up to a 9.46% bitrate reduction with negligible subjective and objective quality loss. The advantage of the proposed method is the high quality adapted for a high-definition video application.

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.