Adaptive and High-Quality Multimedia Streaming

所有形式的多媒体流(包括YouTube和Netflix)占北美高峰互联网流量的58.6%。除了飙升的需求外，由于不同的连接类型(例如WiFi、有线局域网和蜂窝)以及终端用户设备上不同的硬件规范，不同的和波动的网络条件对多媒体流服务的适应性提出了更高的要求。虽然有一些解决方案，如基于HTTP的动态自适应流(DASH)和苹果的HTTP Live流(HLS)，可以满足日益增长的需求并使多媒体流服务更具适应性，但没有一个解决方案充分利用所有可用的网络资源来以尽可能好的质量提供视频。例如，即使从服务提供商到最终用户设备的可用带宽是400 Kbps，足以提供比标准清晰度质量(192 Kpbs比特率)更好的视频，也只会传送标准清晰度视频，因为在标准清晰度和高清晰度(2 Mbps比特率)之间没有中间质量级别。提供连续的视频质量选择方案是具有挑战性的，因为代码转换(对于每个质量级别)是一个耗时且占用大量CPU的过程。在这个研究项目中，我提出了一种新的多媒体流媒体设计方案，它可以流传输高质量(接近可能的最佳质量水平)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，我将在此基础上设计提供细粒度视频质量选择的质量适配算法。最后，研究将开发新的流算法和协议，将新的转码算法和质量自适应算法结合在一起。与现有的流媒体解决方案不同，新的设计将提供定制的流媒体服务，根据终端用户设备的网络状况和状态以尽可能最佳的质量流媒体视频。所有研究成果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媒体服务的服务质量(Quality-of-Service)奠定基础。它将提供解决方案，以应对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，并将激励流媒体内容提供商(如电视台和类似Netflix的服务)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).