论文标题
准备与Segue流媒体的视频
Prepare your video for streaming with Segue
论文作者
论文摘要
我们在视频流中确定了新的机会，涉及离线视频块和在线费率适应的共同考虑。由于视频的复杂性随着时间的流逝而变化，因此某些零件更有可能在播放期间使用特定的速率适应算法造成性能障碍。为了解决此类问题，我们提出了SEGUE，该问题仔细地使用了可变的视频段，并增加了具有其他比特率轨道的特定段。我们方法的主要新颖性是根据视频的时间变化和随时间推移的预期速率适应行为做出此类决策。我们建议并实施几种适应性感知的方法。我们的结果表明，SEGUE大大降低了拒绝和质量的波动，同时保持了视频质量。 SEGUE平均将QoE提高9％，在低频带宽度条件下提高22％。最后，我们将问题框架视为有关算法和设计创新的新线程的第一步，并在视频流中设计创新，并为读者提供了一些有趣的开放问题。
We identify new opportunities in video streaming, involving the joint consideration of offline video chunking and online rate adaptation. Due to a video's complexity varying over time, certain parts are more likely to cause performance impairments during playback with a particular rate adaptation algorithm. To address such an issue, we propose Segue, which carefully uses variable-length video segments, and augment specific segments with additional bitrate tracks. The key novelty of our approach is in making such decisions based on the video's time-varying complexity and the expected rate adaptation behavior over time. We propose and implement several methods for such adaptation-aware chunking. Our results show that Segue substantially reduces rebuffering and quality fluctuations, while maintaining video quality delivered; Segue improves QoE by 9% on average, and by 22% in low-bandwidth conditions. Finally, we view our problem framing as a first step in a new thread on algorithmic and design innovation in video streaming, and leave the reader with several interesting open questions.
