Transport Layer Optimizations for Heterogeneous Wireless Multimedia Networks

The explosive growth of the Internet during the last few years, has been propelled by the TCP/IP protocol suite and the best effort packet forwarding service. However, quality of service (QoS) is far from being a reality especially for multimedia services like video streaming and video conferencing. In the case of wireless and mobile networks, the problem becomes even worse due to the physics of the medium, resulting into further deterioration of the system performance. Goal of this dissertation is the systematic development of comprehensive models that jointly characterize the performance of transport protocols and media delivery in heterogeneous wireless networks. At the core of our novel methodology, is the use of analytical models for driving the design of media transport algorithms, so that the delivery of conversational and non-interactive multimedia data is enhanced in terms of throughput, delay, and jitter. More speciffically, we develop analytical models that characterize the throughput and goodput of the transmission control protocol (TCP) and the transmission friendly rate control (TFRC) protocol, when CBR and VBR multimedia workloads are considered. Subsequently, we enhance the transport protocol models with new parameters that capture the playback buffer performance and the expected video distortion at the receiver. In this way a complete end-to-end model for media streaming is obtained. This model is used as a basis for a new algorithm for rate-distortion optimized mode selection in video streaming appli- cations. As a next step, we extend the developed models for the aforementioned protocols, so that heterogeneous wireless networks can be accommodated. Subsequently, new algorithms are proposed in order to enhance the developed media streaming algorithms when heterogeneous wireless networks are also included. Finally, the aforementioned models and algorithms are extended for the case of concurrent multipath media transport over several hybrid wired/wireless links.Ph.D.Committee Chair: Vijay Madisetti; Committee Member: Raghupathy Sivakumar; Committee Member: Sudhakar Yalamanchili; Committee Member: Umakishore Ramachandran; Committee Member: Yucel Altunbasa

Extending ADR mechanism for LoRa enabled mobile end-devices

A considerable percentage of Internet of Things end-devices are characterised by mobility, a feature that adds extra complexity to protocols used in Wireless Sensor Networks. LoRa is one of the newly introduced wireless sensor protocols, capable of delivering messages in long distances and consuming low energy, features that make it proper for low cost devices. Although LoRa was introduced as a technology for stationary devices, it can also be used for mobile devices of low speed. In this paper, we introduce an enhancement to Adaptive Data Rate (ADR) mechanism to enable mobile LoRa, by improving the connection reliability of mobile end-devices, while keeping energy consumption at low levels. Firstly, we propose the Linear Regression-ADR (LR-ADR) mechanism for the Network Server side to smooth the Signal to Noise Ratio (SNR) estimates per gateway and predict the SNR of the next transmission. Secondly, we propose the Linear Regression + ADR (LR+ADR) mechanism, an adaptive method for the end-device side to regain the connectivity faster with the Network Server. We conducted simulation modelling to evaluate the performance of our implementation while we compared our results with four alternative solutions ADR, ADR+, EMA-ADR, G-ADR. The results prove that our first approach (LR-ADR) performs better than the best competitor, and our second approach (LR+ADR) brings an additional improvement in terms of Packet Delivery Ratio (PDR), while they retain the Energy Consumption per Packet Delivered (ECPD) at low levels. In particular, in a scenario that mimics real world conditions, LR+ADR presents an increase of up to 520% for PDR compared to the original ADR and an improvement of up to 38% compared to the best competitor (G-ADR). Moreover, it reduces ECPD up to 74% compared to the original ADR, while keeping it at the same level with the best competitor (G-ADR