Modern vehicles are evolving with more electronic components than ever before (In this paper, “vehicle” means “automotive vehicle.” It is also equal to “car.”) One notable example is graphical processing unit (GPU), which is a key component to implement a digital cluster. To implement the digital cluster that displays all the meters (such as speed and fuel gauge) together with infotainment services (such as navigator and browser), the GPU needs to be virtualized; however, GPU virtualization for the digital cluster has not been addressed yet. This paper presents a Virtualized Automotive DIsplay (VADI) system to virtualize a GPU and its attached display device. VADI manages two execution domains: one for the automotive control software and the other for the in-vehicle infotainment (IVI) software. Through GPU virtualization, VADI provides GPU rendering to both execution domains, and it simultaneously displays their images on a digital cluster. In addition, VADI isolates GPU from the IVI software in order to protect it from potential failures of the IVI software. We implement VADI with Vivante GC2000 GPU and perform experiments to ensure requirements of International Standard Organization (ISO) safety standards. The results show that VADI guarantees 30 frames per second (fps), which is the minimum frame rate for digital cluster mandated by ISO safety standards even with the failure of the IVI software. It also achieves 60 fps in a synthetic workload.

This paper presents a new Scalable Video Codec (SVC)-based hybrid adaptive video streaming scheme, named HAVS, for mobile devices in wireless environments. The proposed approach takes two existing video streaming technologies, viz., progressive download and adaptive streaming, and switches them in a hybrid manner. To this end, HAVS employs the H.264/SVC encoding scheme, where each video chunk is encoded into one base layer and several enhancement layers. Since clients request the base layer every time a video is streamed, HAVS performs progressive download for the base layer and adaptive streaming for the enhancement layers. Through wireless test-bed experiments, it is demonstrated that the proposed scheme can be easily implemented on mobile devices without any server-side modification. This scheme effectively prevents video freeze thereby providing better quality video streaming than the existing non-hybrid streaming technologies.