The modern urban landscape is becoming increasingly connected, from distributed vehicular safety systems to in-vehicle centralized entertainment. These technologies face network challenges related to the high mobility of vehicles and people. Such mobility raises new challenges. On-board detection of unsafe driving activities lacks a holistic view of the road situation while cloud-offloaded detection faces scalability and context-awareness issues. In terms of in-vehicle entertainment, the connection to remote gaming and VR servers encounters variable bandwidth, latency, and packet losses that affect the gaming and VR experience.

This thesis presents four research contributions to provide reliable real-time content streaming for driving awareness (in the physical world), mobile cloud gami...[ Read more ]

The modern urban landscape is becoming increasingly connected, from distributed vehicular safety systems to in-vehicle centralized entertainment. These technologies face network challenges related to the high mobility of vehicles and people. Such mobility raises new challenges. On-board detection of unsafe driving activities lacks a holistic view of the road situation while cloud-offloaded detection faces scalability and context-awareness issues. In terms of in-vehicle entertainment, the connection to remote gaming and VR servers encounters variable bandwidth, latency, and packet losses that affect the gaming and VR experience.

This thesis presents four research contributions to provide reliable real-time content streaming for driving awareness (in the physical world), mobile cloud gaming (in the virtual world), and a reality-check of the Metaverse between physical and virtual worlds.

The first contribution is CAD3, a distributed collaborative architecture for road-aware and driver-aware anomaly driving detection and real-time warning dissemination. CAD3 exploits the pervasive deployment of roadside units, and combines collaborative and context-aware computation with low-latency communication to detect unsafe driving behaviors and warn the drivers of nearby vehicles in real-time.

The second contribution is Nebula, an end-to-end cloud gaming architecture to minimize the impact of network conditions on the user experience. Nebula relies on a heuristic algorithm and end-to-end distortion model to dynamically adapt the video bitrate and redundancy based on the measured network conditions.

The third contribution is MERA, an edge-assisted learning-based end-to-end cloud gaming architecture to adapt the video bitrate to the network constraints. MERA relies on the transition, state-to-action mapping, then rewarding with a multi-objective reward function to maximize the user QoE.

The fourth contribution is Metaversity, a reality check of social VR platforms. It uncovers the network footprint and its impact on latency, identifies the key bottleneck towards mixed-mode metaverse, and highlights the common pipeline between the platforms.

The fourth contribution is Metaversity, a reality check of a social VR platforms. It uncovers the network footprint and its impact on latency, identifies the key bottleneck towards mixed-mode metaverse, and highlights the common pipeline between the platforms.

The first three contributions develop end-to-end content streaming architectures for time-critical applications. The fourth contribution includes these aspects within the wider topic of metaverse enabling technologies. While CAD3, NEBULA, and MERA focus on minimizing local latencies, Metaversity uncovers the impact of the network footprint of metaverse technology on latency and scalability.

The contributions spread across Milgram’s Reality-Virtuality spectrum. The first contribution primarily targets the physical world (CAD3 using vehicular communication). The second then moves into systems enabling entirely virtual experiences (Nebula and MERA using video-mediated communication for cloud gaming). The last contributes to the mixed physical and virtual world (Metaversity using avatar-mediated communication) in the metaverse.