Adaptive and High-Quality Multimedia Streaming

各种形式的多媒体流媒体（包括YouTube和Netflix）占北美互联网峰值流量的58.6%。 除了飙升的需求之外，由于不同的连接类型（例如，WiFi、有线LAN和蜂窝）以及终端用户设备上的各种硬件规范对多媒体流服务提出了更高的自适应性挑战。 虽然有一些解决方案，如HTTP动态自适应流媒体（DASH）和Apple的HTTP实时流媒体（HLS），以满足不断增长的需求，并使多媒体流媒体服务更具适应性，但它们都没有充分利用所有可用的网络资源来提供最佳质量的视频。例如，即使从服务提供商到终端用户设备的可用带宽是400 Kbps，足以提供比标准清晰度质量更好的视频（在192 Kpbs比特率下），也将仅传送标准清晰度视频，因为在标准清晰度和高清晰度之间没有中间质量水平（在2 Mbps比特率下）。 提供连续的视频质量选择方案是具有挑战性的，因为转码（对于每个质量等级）是耗时且CPU密集的过程。根据动态网络条件选择适当的质量水平也具有挑战性。*在这项研究计划中，我提出了一种新的多媒体流设计，流高质量（接近最佳质量水平）的视频。新设计引入了基于云的视频转码算法，以准备不同质量级别的视频。 它还包括一个定义质量级别的新模型，基于此，我将设计提供细粒度视频质量选择的质量自适应算法。最后，本研究将结合新的转码算法和质量自适应算法开发新的流传输算法和协议。与现有的流媒体解决方案相比，新设计将提供定制的流媒体服务，根据网络条件和最终用户设备的状态以最佳质量传输视频。 所有研究成果都将在真实的云和网络中使用标准视频播放器进行实施和评估。*该研究计划将提供创新的内容感知流媒体设计，提供自适应和高质量的流媒体服务。这将为提高加拿大多媒体流服务的QoS（服务质量）奠定基础。它将提供解决方案，以科普加拿大主要运营商网络（如Shaw，Rogers和Telus）对高质量流媒体服务日益增长的需求，并将为流媒体内容提供商（如电视台和Netflix类服务）提供新的流媒体服务。

Multimedia streaming in all forms (including YouTube and Netflix) constitutes 58.6% of peak Internet traffic in North America. In addition to the soaring demand, the heterogeneous and fluctuating network condition, due to different connection types (e.g., WiFi, wired LAN, and cellular), and diverse hardware specifications on end-user devices challenge multimedia streaming services to be more adaptive. Though there are solutions, such as Dynamic Adaptive Streaming over HTTP (DASH) and Apple's HTTP Live Streaming (HLS), to meet the increasing demand and to make multimedia streaming services more adaptive, none of them fully utilizes all available network resources to deliver the video in the best possible quality. For example, even if the available bandwidth from a service provider to an end-user device is 400 Kbps, sufficient to serve a video better than the standard definition quality (at 192 Kpbs bitrate), only standard definition video will be delivered since there is no intermediate quality level between standard definition and high definition (at 2 Mbps bitrate). It is challenging to provide a continuous video quality selection scheme since transcoding (for each quality level) is a time consuming and CPU intensive process. It is also challenging to select the appropriate quality level subject to the dynamic network condition.***In this research program, I propose a new design for multimedia streaming that streams high-quality (approximating the best possible quality level) video. The new design introduces cloud-based video transcoding algorithms to prepare videos at different quality levels. It also includes a new model defining quality levels, based on which I will design quality adaptation algorithms offering fine-granular video quality selection. Finally, the research will develop new streaming algorithms and protocols incorporating the new transcoding algorithms and quality adaptation algorithms. In contrast to existing streaming solutions, the new design will provide a custom-fit streaming service that streams videos in the best possible quality subject to the network condition and status of end-user devices. All research results will be implemented and evaluated with standard video players in real clouds and networks.***This research program will deliver an innovative content-aware streaming design offering adaptive and high-quality streaming services. It will lay the foundation for advancing the QoS (Quality-of-Service) of multimedia streaming services in Canada. It will provide solutions to cope with increasing demand for high-quality streaming service in major carrier networks across Canada (such as Shaw, Rogers, and Telus), and will inspire new streaming services for streaming content providers (such as TV stations and Netflix-like services).**