There is a large increase in the surge of data over Internet due to the increasing demand on multimedia content. It is estimated that 80% of Internet traffic will be video by 2022, according to a recent study. At the same time, IoT devices on Internet will double the human population. While infrastructure standards on IoT are still nonexistent, enterprise solutions tend to encourage cloud-based solutions, causing an additional surge of data over the Internet. This study proposes solutions to bring video traffic and IoT computation back to the edges of the network, so that costly Internet infrastructure upgrades are not necessary. An efficient way to prevent the Internet surge over the network for IoT is to push the application specific computation to the edge of the network, close to where the data is generated, so that large data can be eliminated before being delivered to the cloud. In this study, an event query language and processing environment is provided to process events from various devices. The query processing environment brings the application developers, sensor infrastructure providers and end users together. It uses boolean events as the streaming and processing units. This addresses the device heterogeneity and pushes the data-intense tasks to the edge of network. The second focus of the study is Video-on-Demand applications. A characteristic of VoD traffic is its high redundancy. Due to the demand on popular content, the same video traffic flows through Internet Service Provider's network as overlapping but separate streams. In previous studies on redundancy elimination, overlapping streams are merged into each other in link-level by receiving the packet only for the first stream, and re-using it for the subsequent duplicated streams. In this study, we significantly improve these techniques by introducing a merger-aware routing method. Our final focus is increasing utilization of Content Delivery Network (CDN) servers on the edge of network to reduce the long-distance traffic. The proposed system uses Software Defined Networks (SDN) to route adaptive video streaming clients to the best available CDN servers in terms of network availability. While performing the network assistance, the system does not reveal the video request information to the network provider, thus enabling privacy protection for encrypted streams. The request routing is performed in segment level for adaptive streaming. This enables to re-route the client to the best available CDN without an interruption if network conditions change during the stream.


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Graduation Date





Hua, Kien


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Computer Science

Degree Program

Computer Science









Release Date

November 2022

Length of Campus-only Access

3 years

Access Status

Doctoral Dissertation (Open Access)