Video Streaming In Vehicular Networks Research Articles

Intelligent Caching for Mobile Video Streaming in Vehicular Networks with Deep Reinforcement Learning

Caching-enabled multi-access edge computing (MEC) has attracted wide attention to support future intelligent vehicular networks, especially for delivering high-definition videos in the internet of vehicles with limited backhaul capacity. However, factors such as the constrained storage capacity of MEC servers and the mobility of vehicles pose challenges to caching reliability, particularly for supporting multiple bitrate video streaming caching while achieving considerable quality of experience (QoE). Motivated by the above challenges, in this paper, we propose an intelligent caching strategy that takes into account vehicle mobility, time-varying content popularity, and backhaul capability to improve the QoE of vehicle users effectively. First, based on the mobile video mean opinion score (MV-MOS), we designed an average download percentage (ADP) weighted QoE evaluation model. Then, the video content caching problem is formulated as a Markov decision process (MDP) to maximize the ADP weighted MV-MOS. Owing to the prior knowledge of video content popularity and channel state information that may not be available at the road side unit in practical scenarios, we propose a deep reinforcement learning (DRL)-based caching strategy to solve the problem while achieving a maximum ADP weighted MV-MOS. To accelerate its convergence speed, we further integrate the prioritized experience replay, dueling, and double deep Q-network technologies, which improve the performance of DRL algorithm. Numerical results demonstrate that the proposed DRL-based caching strategy significantly improves QoE, and achieves better video delivery reliability compared to existing non-learning approaches.

Source Coding Options to Improve HEVC Video Streaming in Vehicular Networks

Video delivery in Vehicular Ad-hoc NETworks has a great number of applications. However, multimedia streaming over this kind of networks is a very challenging issue because (a) it is one of the most resource-demanding applications; (b) it requires high bandwidth communication channels; (c) it shows moderate to high node mobility patterns and (d) it is common to find high communication interference levels that derive in moderate to high loss rates. In this work, we present a simulation framework based on OMNeT++ network simulator, Veins framework, and the SUMO mobility traffic simulator that aims to study, evaluate, and also design new techniques to improve video delivery over Vehicular Ad-hoc NETworks. Using the proposed simulation framework we will study different coding options, available at the HEVC video encoder, that will help to improve the perceived video quality in this kind of networks. The experimental results show that packet losses significantly reduce video quality when low interference levels are found in an urban scenario. By using different INTRA refresh options combined with appropriate tile coding, we will improve the resilience of HEVC video delivery services in VANET urban scenarios.

ICN based vehicle-to-cloud delivery for multimedia streaming in urban vehicular networks

Mobile multimedia streaming is an open topic in vehicular environment. Due to the high intermittent links, it has become a critical challenge to deliver high quality video streaming in vehicular networks. In this paper, we reform the Information Centric Networking (ICN) concept for multimedia delivery in urban vehicular networks. By leveraging the ICN perspective, we highlight that vehicular peers can obtain multimedia chunks via the vehicle-to-cloud (V2C) approach to improve the delivery quality. Based on this, we propose a lightweight multipath selection strategy to guide the network system to adaptively adjust the forwarding means. Extensive simulations show that the proposed solution can optimize the utilization of network paths, lighten network loads as well as avoid wasting resources.

Channel Allocation for Adaptive Video Streaming in Vehicular Networks

Video services in vehicular networks play an important role in future intelligent transportation systems and vehicular infotainment systems. Yet, at the presence of other services with high priorities, the remaining radio resources for video services are highly dynamic. To support video service of multiple vehicles in vehicular networks, we propose a joint channel allocation and adaptive video streaming algorithm that makes the vehicles compete for channel access opportunities and to request video data with a proper visual quality according to their utilities. A vehicle's request is determined by taking several key factors into consideration, including the location and the velocity of the vehicle, the activity of the high-priority services, the intensity of the competition among multiple vehicles, and the smoothness requirement of visual quality. Simulation results show that the proposed algorithm is superior to the existing algorithms in both interruption ratio and visual quality.

A Multipath Video Streaming Solution for Vehicular Networks with Link Disjoint and Node-disjoint

The high quality video streaming in vehicular networks is an urgent topic to provide services on safety and infotainment on the road. To provide high quality video streaming, Forward Error Correction (FEC) is one of the most popular approaches to ensure the needed quality for video streaming by generating duplicated packets. However, several factors might cause problems to FEC in a VANET, like the limited resources of wireless networks, a highly dynamic topology and the large amount of data in video streaming. The duplicated packets might exceed the network capacity. Therefore, in this work, the retransmission mechanism is used to ensure the transmissions, rather than the FEC. The main problem of the retransmission mechanism is the delay. A multi-path solution based on a disjoint algorithm is proposed to reduce the interference and contention, leading to a higher transmission rate and an acceptable delay. In this solution, only I-frames are transmitted through the TCP protocol, and the inter-frames are transmitted through the UDP protocol. To improve the delay of TCP transmissions, a TCP-ETX algorithm is integrated to select the suitable path for TCP transmissions. Simulations are conducted and several results are presented by comparing them to other protocols. Based on the simulation results, the designed multi-path solution protocol provides a higher video quality with a reasonable delay than that of the other protocols.