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Error Resilient Coding Techniques for Video Delivery over Vehicular Networks

机译:通过车载网络进行视频传输的错误弹性编码技术

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摘要

Nowadays, more and more vehicles are equipped with communication capabilities, not only providing connectivity with onboard devices, but also with off-board communication infrastructures. From road safety (i.e., multimedia e-call) to infotainment (i.e., video on demand services), there are a lot of applications and services that may be deployed in vehicular networks, where video streaming is the key factor. As it is well known, these networks suffer from high interference levels and low available network resources, and it is a great challenge to deploy video delivery applications which provide good quality video services. We focus our work on supplying error resilience capabilities to video streams in order to fight against the high packet loss rates found in vehicular networks. So, we propose the combination of source coding and channel coding techniques. The former ones are applied in the video encoding process by means of intra-refresh coding modes and tile-based frame partitioning techniques. The latter one is based on the use of forward error correction mechanisms in order to recover as many lost packets as possible. We have carried out an extensive evaluation process to measure the error resilience capabilities of both approaches in both (a) a simple packet error probabilistic model, and (b) a realistic vehicular network simulation framework. Results show that forward error correction mechanisms are mandatory to guarantee video delivery with an acceptable quality level , and we highly recommend the use of the proposed mechanisms to increase even more the final video quality.
机译:如今,越来越多的车辆配备了通信功能,不仅提供了与车载设备的连接,而且还提供了非车载通信基础设施。从道路安全(即,多媒体电子呼叫)到信息娱乐(即,视频点播服务),可以在车辆网络中部署许多应用和服务,其中视频流是关键因素。众所周知,这些网络遭受高干扰水平和低可用网络资源的困扰,并且部署提供高质量视频服务的视频传递应用程序是一个巨大的挑战。我们的工作重点是为视频流提供错误恢复能力,以应对车载网络中的高丢包率。因此,我们提出源编码和信道编码技术的结合。前者通过帧内刷新编码模式和基于图块的帧划分技术应用于视频编码过程。后者基于前向纠错机制的使用,以便恢复尽可能多的丢失数据包。我们已经进行了广泛的评估过程,以在(a)简单的分组错误概率模型和(b)实际的车辆网络仿真框架中测量两种方法的错误恢复能力。结果表明,前向纠错机制对于确保视频质量达到可接受水平是必不可少的,我们强烈建议使用建议的机制来提高最终视频质量。

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