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Title: Optimizing video delivery on mobile networks
Authors: Chen, Jiasi
Advisors: Chiang, Mung
Contributors: Electrical Engineering Department
Keywords: mobile networks
wireless networks
Subjects: Computer science
Electrical engineering
Issue Date: 2015
Publisher: Princeton, NJ : Princeton University
Abstract: Mobile video is a key driver of lifestyle in the networked age. Reconciling demanding video applications with scarce wireless spectrum, however, is no easy task for network researchers and operators. Traditional mobile resource management techniques may interact poorly with client-driven control of video applications. In this thesis, I propose solutions that rethink the interactions between the client’s decisions and the network operator’s optimizations. Through mathematical models and real-world experiments, I am to achieve fair provisioning of user quality of experience (QoE) and efficient utilization of wireless resources. A popular method for clients to improve video QoE under changing wireless conditions is by changing the video bitrate to match available bandwidth (DASH). When multiple DASH clients independently compete for resources on a shared wireless cellular link, problems arise in fairness, stability, and resource utilization. To overcome these problems, I propose a cellular resource management framework for adaptive video flows, AVIS, and validate our design on a 4G testbed. Cellular multicast is another technique to improve wireless resource efficiency. However, multicast aims to deliver content at the same rate to all users, whereas different wireless users may have different physical-layer rates depending on their signal strength. My work on LTE multicast (eMBMS) explores how to optimally partition users and allocate resources to alleviate this conflict. Finally, the vast growth of video traffic is clashing with usage-based pricing of cellular data plans imposed by network operators. Is there a way for users to stay within their monthly data quotas without suffering a noticeable degradation in video quality? QAVA is an online video adaptation system that manages this tradeoff by leveraging compressibility of different types of videos and by predicting consumer usage behavior throughout the billing cycle. I demonstrate QAVA’s efficacy using trace-driven simulations and an Android prototype.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Electrical Engineering

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