Towards Cost-Efficient Video Transcoding in Media Cloud: Insights Learned From User Viewing Patterns
Towards Cost-Efficient Video Transcoding in Media Cloud: Insights Learned From User Viewing Patterns
复制标题
在媒体云中实现经济高效的视频转码：从用户观看模式中获得的见解
DOI：
10.1109/tmm.2015.2438713
复制
发表时间：
2015-08-01
影响因子：
7.3
通讯作者：
Zhu, Wenwu
中科院分区：
文献类型：
--
作者：
Gao, Guanyu;Zhang, Weiwen;Zhu, Wenwu
自适应比特率流（ABR）系统中的视频转码需要支持异构设备和不同网络上的视频流。但是，这可能会产生巨大的成本。与此同时，大多数观众在20%的持续时间内终止观看会话;每个视频只有一小部分被消耗。基于这种用户观看模式，我们提出了一种用于媒体云中内容管理的部分转码方案。具体地，每个内容被编码成不同的比特率并被分割成片段。有些段存储在缓存中，导致存储成本;其他段在缓存未命中的情况下在线转码，导致计算成本。我们的目标是通过确定一个片段是否应该被缓存或在线转码来最大限度地减少长期的总成本。我们将其表述为一个约束随机优化问题。利用李雅普诺夫优化框架和拉格朗日松弛，我们设计了一个在线算法，可以获得可证明的上界内的最优解。实验表明，我们提出的方法可以减少30%的操作成本，相比缓存所有的片段的计划。
Video transcoding in an adaptive bitrate streaming (ABR) system is demanded to support video streaming over heterogenous devices and varying networks. However, it could incur a tremendous cost. Meanwhile, most viewers terminate viewing sessions within 20% of their durations; only a small fraction of each video is consumed. Built upon this user viewing pattern, we propose a Partial Transcoding Scheme for content management in media clouds. Particularly, each content is encoded into different bitrates and split into segments. Some of the segments are stored in cache, resulting in storage cost; others are transcoded online in the case of cache miss, resulting in computing cost. We aim to minimize the long-term overall cost by determining whether a segment should be cached or transcoded online. We formulate it as a constrained stochastic optimization problem. Leveraging Lyapunov optimization framework and Lagrangian relaxation, we design an online algorithm which can achieve the optimal solution within provable upper bounds. Experiments demonstrate that our proposed method can reduce 30% of operational cost, compared with the scheme of caching all the segments.