Adaptive HTTP video streaming techniques are rapidly becoming the main method for video delivery over the Internet. From a conceptual viewpoint, adaptive HTTP video streaming systems enable adaptation of the video quality according to network conditions (link-awareness), content characteristics (content-awareness), user preferences (user-awareness) or device capabilities (device awareness). Proprietary adaptive HTTP video streaming platforms from Apple, Adobe and Microsoft preceded the completion of a standard for adaptive HTTP video streaming, i.e., the MPEG-DASH standard. The dissertation presents modeling approaches, experiments, simulations and subjective tests tightly related to adaptive HTTP video streaming with particular emphasis on the MPEG-DASH standard. Different case studies are investigated through novel models based on analytical and simulation approaches. In particular, adaptive HTTP video streaming over Long Term Evolution (LTE) networks, over cloud infrastructure, and streaming of medical videos are investigated and the relevant benefits and drawbacks of using adaptive HTTP video streaming for these cases are highlighted. Further, mathematical tools and concepts are used to acquire quantifiable knowledge related to the HTTP/TCP communication protocol stack and to investigate dependencies between adaptive HTTP video streaming parameters and the underlying Quality of Service (QoS) and Quality of Experience (QoE). Additionally, a novel method and model for QoE assessment are proposed, derived in a specific experimental setup. A more general setup is then considered and a QoE metric is derived. The QoE metric expresses the users' quality for adaptive HTTP video streaming by taking into consideration rebuffering, video quality and content-related parameters. In the end, a novel analytical model that captures the user's perception of quality via the experienced delay during streaming navigation is derived. The contributions in this dissertation and the relevant conclusions are obtained by simulations, experimental demo setups, subjective tests and analytical modeling.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:713141 |
| Date | January 2016 |
| Creators | Ognenoski, Ognen |
| Publisher | Kingston University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.kingston.ac.uk/37874/ |
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