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。根据动态的网络条件选择适当的质量级别也是具有挑战性的。

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.