Adaptive Wireless Video Streaming Based on Edge Computing: Opportunities and Approaches
Adaptive Wireless Video Streaming Based on Edge Computing: Opportunities and Approaches
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DOI：
10.1109/tsc.2018.2828426
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发表时间：
2019-09
影响因子：
8.1
通讯作者：
Desheng Wang;Yanrong Peng;Xiaoqiang Ma;Wenting Ding;Hongbo Jiang;Fei Chen;Jiangchuan Liu
中科院分区：
文献类型：
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作者：
Desheng Wang;Yanrong Peng;Xiaoqiang Ma;Wenting Ding;Hongbo Jiang;Fei Chen;Jiangchuan Liu
HTTP 上的动态自适应流媒体 (DASH) 已被广泛采用来处理网络条件和设备功能等用户多样性。在DASH系统中，计算密集型转码是实现视频码率适配的关键技术，云已成为海量视频转码的首选解决方案。然而，基于云的解决方案具有以下两个缺点。首先，视频流转码后有多个版本，这增加了穿越核心网络的网络流量。其次，转码策略通常是固定的，无法灵活适应观看者的动态变化。考虑到移动用户占总用户的很大一部分，通常会不时经历动态的网络状况，因此自适应无线转码非常重要。为此，我们通过在基站附近部署边缘转码服务器，提出了一种基于新兴边缘计算范式的自适应无线视频转码框架。通过这种设计，核心网只需将源视频流发送到边缘转码服务器，而不是为每个观看者发送一个流，从而显着减少了核心网的网络流量。同时，我们的边缘转码服务器与基站配合，根据获取的用户信道条件对视频进行更细粒度的转码，智能调整转码策略以应对时变的无线信道。为了提高带宽利用率，我们还开发了高效的带宽调整算法，自适应地将频谱资源分配给各个移动用户。我们通过广泛的模拟验证了我们提出的基于边缘计算的框架的有效性，这证实了我们框架的优越性。
Dynamic Adaptive Streaming over HTTP (DASH) has been widely adopted to deal with such user diversity as network conditions and device capabilities. In DASH systems, the computation-intensive transcoding is the key technology to enable video rate adaptation, and cloud has become a preferred solution for massive video transcoding. Yet the cloud-based solution has the following two drawbacks. First, a video stream now has multiple versions after transcoding, which increases the network traffic traversing the core network. Second, the transcoding strategy is normally fixed and thus is not flexible to adapt to the dynamic change of viewers. Considering that mobile users, who normally experience dynamic network conditions from time to time, have occupied a very large portion of the total users, adaptive wireless transcoding is of great importance. To this end, we propose an adaptive wireless video transcoding framework based on the emerging edge computing paradigm by deploying edge transcoding servers close to base stations. With this design, the core network only needs to send the source video stream to the edge transcoding server rather than one stream for each viewer, and thus the network traffic across the core network is significantly reduced. Meanwhile, our edge transcoding server cooperates with the base station to transcode videos at a finer granularity according to the obtained users’ channel conditions, which smartly adjusts the transcoding strategy to tackle with time-varying wireless channels. In order to improve the bandwidth utilization, we also develop efficient bandwidth adjustment algorithms that adaptively allocate the spectrum resources to individual mobile users. We validate the effectiveness of our proposed edge computing based framework through extensive simulations, which confirm the superiority of our framework.