Over the last decade, the wide deployment of wireless access technologies (e.g. WiFi, 3G, and LTE) and the remarkable growth in the volume of streaming video content have significantly altered the telecommunications field. These developments introduce new challenges to the research community including the need to develop new solutions (e.g. traffic models and transport protocols) to address changing traffic patterns and the characteristics of wireless links and the need for new evaluation methods that generate higher fidelity results under more realistic scenarios.
Unfortunately, for the last two decades, simulation studies have been the main tool for researchers in wireless networks. In spite of the advantages of simulation studies, overall they have had a negative influence on the credibility of published results. In partial response to this simulation crisis, the research community has adopted testing and evaluation using implementation-based experiments. Implementation-based experiments include field experiments, prototypes, emulations, and testbeds. An example of an implementation-based experiment is the MANIAC Challenge, a wireless networking competition that we designed and hosted, which included creation and operation of ad hoc networks using commodity hardware. However, the lack of software tools to facilitate these sorts of experiments has created new challenges. Currently, researchers must practice kernel programming in order to implement networking experiments, and there is an urgent need to lower the barriers of entry to wireless network experimentation.
With respect to the growth in video traffic over wireless networks, the main challenge is a mismatch between the design concepts of current internet protocols (e.g. the Transport Control Protocol (TCP)) and the reality of modern wireless networks and streaming video techniques. Internet protocols were designed to be deployed over wired networks and often perform poorly over wireless links; video encoding is highly loss tolerant and delay-constrained and yet, for reasons of expedience is carried using protocols that emphasize reliable delivery at the cost of potentially high delay.
This dissertation addresses the lack of software tools to support implementation-based networking experiments and the need to improve the performance of video streaming over wireless access networks. We propose a new software tool that allows researchers to implement experiments without a need to become kernel programmers. The new tool, called the Flexible Internetwork Stack (FINS) Framework, is available under an open source license. With our tool, researchers can implement new network layers, protocols, and algorithms, and redesign the interconnections between the protocols. It offers logging and monitoring capabilities as well as dynamic reconfigurability of the modules' attributes and interconnections during runtime. We present details regarding the architecture, design, and implementation of the FINS Framework and provide an assessment of the framework including both qualitative and quantitative comparison with significant previous tools.
We also address the problem of HTTP-based adaptive video streaming (HAVS) over WiFi access networks. We focus on the negative influence of wireless last-hop connections on network utilization and the end-user quality of experience (QoE). We use a cross-layer approach to design three controllers. The first and second controllers adopt a heuristic cross-layer design, while the third controller formulates the HAVS problem as a Markov decision process (MDP). By solving the model using reinforcement learning, we achieved 20% performance improvement (after enough training) with respect to the performance of the best heuristic controller under unstable channel conditions. Our simulation results are backed by a system prototype using the FINS Framework.
Although it may seem predictable to achieve more gain in performance and in QoE by using cross-layer design, this dissertation not only presents a new technique that improves performance, but also suggests that it is time to move cross-layer and machine-learning-based approaches from the research field to actual deployment. It is time to move cognitive network techniques from the simulation environment to real world implementations.
Ph. D.