Supporting Scalable and Resilient Video Streaming Applications in Evolving Networks
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While the demand for video streaming services has risen rapidly in recent years, supporting video streaming service to a large number of receivers still remains a challenging task. Issues of video streaming in the Internet, such as scalability, and reliability are still under extensive research. Recently proposed network contexts such as overlay networks, and mobile ad hoc networks pose even tougher challenges. This thesis focuses on supporting scalable video streaming applications under various network environments. More specifically, this thesis investigates the following problems: i) Server selection in replicated batching video on demand (VoD) systems: we find out that, to optimize the user perceived latency, it is vital to consider the server state information and channel allocation schemes when making server selection decisions. We develop and evaluate a set of server selection algorithms that use increasingly more information. ii) Scalable live video streaming with time shifting and video patching: we consider the problem of how to enable continuous live video streaming to a large group of clients in cooperative but unreliable overlay networks. We design a server-based architecture which uses a combined technique of time-shifting video server and P2P video patching. iii) A Cooperative patching architecture in overlay networks: We design a cooperative patching architecture which shifts video patching responsibility completely to the client side. An end-host retrieves lost data from other end-hosts within the same multicast group. iv) V3: a vehicle to vehicle video streaming architecture: We propose V3, an architecture to provide live video streaming service to driving vehicles through vehicle-to-vehicle (V2V) networks. V3 incorporates a novel signaling mechanism to continuously trigger video sources to send video data back to the receiver. It also adopts a store-carry-and-forward approach to transmit video data in a partitioned network environment. We also develop a multicasting framework that enables live video streaming applications from multiple sources to multiple receivers in V2V networks. A message integration scheme is used to suppress the signaling overhead, and a two-level tree-based routing approach is adopted to forward the video data.