Video streaming has been growing rapidly since the beginning of this century and it is expected to continue growing. With rapid growth of Internet traffic led by video traffic, the Internet busy hours on both mobile and fixed connection segments will double before the end of this decade. Meanwhile, transmission delay is a well-known problem in video streaming and it has been addressed by many prior works that demonstrated the feasibility of reducing packet delays over the Internet by employing a variety of end-to-end techniques. This thesis consists of two parts that introduce new video streaming frameworks over the Internet and over connected-vehicle networks, respectively. Our objective in the first part of this thesis is to improve video streaming over the Internet. The emerging of new technology such as the HTTP-based Adaptive Streaming (HAS) approach has emerged as the dominant framework for video streaming mainly due to its simplicity, firewall friendliness, and ease of deployment. However, recent studies have shown that HAS solutions suffer from major shortcomings, including unfairness, significant bitrate oscillation under different conditions and significant delay. On the other hand, Quality-of-Service (QoS) based mechanisms, most notably multi-priority queue mechanisms such as DiffServ, can provide optimal video experience but at a major cost in complexity within the network. Our objective in this thesis is to design an efficient, low complexity and low delay video streaming framework.We call our proposed Internet streaming framework Erasable Packets within Internet Queues (EPIQ). Our proposed solution is based on a novel packetization of the video content in a way that exploits the inherent multi-priority nature of video. An important notion of our proposed framework is Partially Erasable Packet (PEP) that has two key attributes: (1) Each PEP packet carries multiple segments corresponding to multiple priority levels of the video content; and (2) High priority segments are placed next to the packet header while low-priority segments are placed toward the tail of the PEP packet. Furthermore, to evaluate our framework performance, we developed an analytical model for EPIQ that shows significant improvements when compared to the conventional and multi-priority queue video transmission models. Our proposed solution consists of a new Active Queue Management (AQM) that is similar to the RED algorithm. Under congestion, a best-effort AQM router can simply erase an arbitrary portion of a PEP packet starting from its tail where we denote this process as Partial Erasing (PE). To complement partial erasing in the AQM, a rate control protocol similar to TFRC is proposed to ensure fairness for video and non-video traffic. We demonstrate the viability of the proposed framework by simulating High Definition (HD) Video on Demand (VoD) streaming on the popular network simulator ns-2. Our results show that EPIQ provides improvements in video quality in terms of PSNR by at least 3dB over traditional video streaming formworks. In addition, packet loss ratio and delay jitter performance are comparable to the optimal video streaming mechanism that is offered by multi-priority systems such as DiffServ.The main objective of the second part of the thesis is to develop a vehicle active safety framework that utilizes video streaming and vehicle-to-vehicle (V2V) communication for driver warning. Most prior efforts for V2V safety applications have been limited to sharing vehicle status data between connected vehicles. On the other hand, video streaming has been mainly proposed for video contents sharing between vehicles or dashboard camera sharing.We propose a Cooperative Advanced Driver Assistance System (C-ADAS) where vehicles share visual information and fuse it with local visuals to improve the performance of driver assistance systems. In our proposed system, vehicles share detected objects (e.g., pedestrians, vehicles, cyclists, etc.) and important camera data using the DSRC technology. The vehicle receiving the data from an adjacent vehicle can then fuse the received visual data with its own camera views to create a much richer visual scene. The sharing of data is motivated by the fact that some critical visual views captured by one vehicle are not visible or captured by many other vehicles in the same environment. Sharing such data in real-time provides an invaluable new level of awareness that can significantly enhance a driver-assistance, connected vehicle, and/or autonomous vehicle’s safety-system. The experimental results showed that our proposed system performed as intended and was able to warn drivers ahead of time, and consequently, it could mitigate major accidents and safe lives.
Read
