Reliable and real-time content streaming for cloud and edge computing

PhD Thesis Proposal Defence


Title: "Reliable and real-time content streaming for cloud and edge 
computing"

by

Mr. Ahmad ALHILAL


Abstract:

People commute daily using private cars or public transport. These 
transportation systems are increasingly becoming connected, from 
distributed safety systems to onboard entertainment, and face network 
challenges related to the high mobility of vehicles. People's smartphones, 
vehicles, and roadside units can (inter)communicate (in)directly for road 
safety (requiring ultra-reliable and low latency), driving assistance 
(more relaxed), and entertainment (depending on the application). However, 
the mobility of vehicles and people raises new challenges.

The detection of unsafe driving activities lacks holism when performed 
onboard, scalability, and context-awareness when offloaded to the cloud. 
Besides, the connection to remote gaming servers encounters variable 
bandwidth, latency, and packet loss that affect the gaming experience.

This thesis presents three research contributions to provide reliable 
real-time content streaming for driving awareness and mobile cloud gaming. 
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.

With these contributions, end-to-end content streaming architectures are 
developed for time-critical applications to obtain minimal local latency, 
the time span between driving anomalous events and warning dissemination 
(CAD3) while obtaining high detection accuracy and system scalability, and 
between the user input and playing back the corresponding frame (Nebula 
and  MERA) while maintaining satisfactory video quality.


Date:			Friday, 8 April 2022

Time:                  	4:00pm - 6:00pm

Zoom Meeting: 
https://hkust.zoom.us/j/99249827841?pwd=OTFyc281ZzlVeVdSeEJBRGVidlY0UT09

Committee Members:	Prof. Pan Hui (EMIA, Supervisor)
  			Prof. James Kwok (Chairperson)
 			Prof. Gary Chan
 			Dr. Tristan Braud


**** ALL are Welcome ****