Cheng-Hsin Hsu

Efficient Mobile Multimedia Streaming

Supervisor(s) and Committee member(s): Dr. Joseph Peters, Chair; Dr. Mohamed Hefeeda, Senior Supervisor; Dr. Ramesh Krishnamurti, Supervisor; Dr. Funda Ergun, SFU Examiner; Dr. Charles Krasic, External Examiner

URL: http://www2.fas.sfu.ca/pub/cs/theses/2009/Cheng-HsinHsuPhD.pdf

Modern mobile devices have evolved into small computers that can render multimedia streaming content anywhere and anytime. These devices can extend the viewing time of users and provide more business opportunities for service providers. Mobile devices, however, make a challenging platform for providing high-quality multimedia services. The goal of this thesis is to identify these challenges from various aspects, and propose efficient and systematic solutions to solve them. In particular, we study mobile video broadcast networks in which a base station concurrently transmits multiple video streams over a shared air medium to many mobile devices. We propose algorithms to optimize various quality-of-service metrics, including streaming quality, bandwidth efficiency, energy saving, and channel switching delay. We analytically analyze the proposed algorithms, and we evaluate them using numerical methods and simulations. In addition, we implement the algorithms in a real testbed to show their practicality and efficiency. Our analytical, simulation, and experimental results indicate that the proposed algorithms can: (i) maximize energy saving of mobile devices, (ii) maximize bandwidth efficiency of the wireless network, (iii) minimize channel switching delays on mobile devices, and (iv) efficiently support heterogeneous mobile devices. Last, we give network operators guidelines on choosing solutions suitable for their mobile broadcast networks, which allow them to provide millions of mobile users much better viewing experiences, attract more subscribers, and thus increase the revenues.

Network Systems Lab at SFU

URL: http://nsl.cs.sfu.ca

The Network Systems Lab at Simon Fraser University is led by Dr. Mohamed Hefeeda, and is affiliated with the Network Modeling Group at Simon Fraser University. We are interested in the broad areas of computer networking and multimedia systems. We develop algorithms and protocols to enhance the performance of networks, especially the Internet, and to efficiently distribute multimedia content (e.g., video and audio objects) to large scale user communities.

Our current research interests include multimedia networking, peer-to-peer (P2P) systems, wireless sensor networks, network security, and high performance computing. Brief descriptions are given below.

In multimedia networking, we are focusing on distributed streaming in dynamic environments and for heterogeneous clients. Our goal is to analyze and understand scalable coding techniques, and to design several optimization and streaming algorithms to make the best possible use of them in real multimedia systems. This will yield better quality for users, and more efficient utilization of network and server resources. We are also designing algorithms to optimize streaming quality for wireless and mobile clients.

In P2P systems, we are exploring the applicability of the P2P paradigm to build cost-effective content distribution systems. Problems such as sender selection, adaptive object replication, and content caching are being studied. We are also developing models to analyze the new characteristics of the P2P traffic and the impact of these characteristics on the cache replacement policies and object replication strategies. Furthermore, we are devising analytic models to study the dynamics of the P2P system capacity and the impact of various parameters on it.

In network security, we are exploring network monitoring techniques to detect and thwart intrusion and denial-of-service attacks in their early stages by observing unusual traffic patterns injected by such attacks. We are studying the security of multimedia streaming systems that employ multi-layer and fine-grain scalable video streams.

In high performance computing, we are exploring the opportunities of utilizing new architectures such as GPUs, multi-core processors, and distributed clusters (cloud computing) to efficiently solve research problems related to multimedia content analysis, large-scale data analysis, and machine learning techniques.

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