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...[
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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.
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