Abstract
Due to the growing demand for video streaming services, providers have to deal with increasing resource requirements for increasingly heterogeneous environments. To mitigate this problem, many works have been proposed which aim to ( ${i}$ ) improve cloud/edge caching efficiency, ( ii ) use computation power available in the cloud/edge for on-the-fly transcoding, and ( iii ) optimize the trade-off among various cost parameters, e.g. , storage, computation, and bandwidth. In this paper, we propose LwTE , a novel ${L}$ ight- ${w}$ eight ${T}$ ranscoding approach at the ${E}$ dge, in the context of HTTP Adaptive Streaming (HAS). During the encoding process of a video segment at the origin side, computationally intense search processes are going on. The main idea of LwTE is to store the optimal results of these search processes as metadata for each video bitrate and reuse them at the edge servers to reduce the required time and computational resources for on-the-fly transcoding. LwTE enables us to store only the highest bitrate plus corresponding metadata (of very small size) for unpopular video segments/bitrates. In this way, in addition to the significant reduction in bandwidth and storage consumption, the required time for on-the-fly transcoding of a requested segment is remarkably decreased by utilizing its corresponding metadata; unnecessary search processes are avoided. Popular video segments/bitrates are being stored. We investigate our approach for Video-on-Demand (VoD) streaming services by optimizing storage and computation (transcoding) costs at the edge servers and then compare it to conventional methods (store all bitrates, partial transcoding). The results indicate that our approach reduces the transcoding time by at least 80% and decreases the aforementioned costs by 12% to 70% compared to the state-of-the-art approaches.
Highlights
In recent years, video streaming has developed very quickly and, according to the Cisco Visual Networking Index [1], will gain up to 88% of the total Internet traffic by 2022
The Light-weight Transcoding at the Edge (LwTE) approach is applicable for cloud platforms, we focus on the edge servers in the sense of, e.g., multi-access edge computing in 5G networks, to reveal its potential and capabilities
Thanks to the extracted metadata, LwTE reduces the total cost by 60% through storing 2% of segments/bitrates as the popular set and performing light-weight transcoding for the other ones
Summary
Video streaming has developed very quickly and, according to the Cisco Visual Networking Index [1], will gain up to 88% of the total Internet traffic by 2022. Some studies try to minimize video streaming costs by combining on-the-fly transcoding and pre-transcoding approaches [7], [10], [11]. They try to minimize costs by trading off storage costs and computation costs, considering various constraints. A request for an unpopular segment/bitrate will be served by on-the-fly transcoding from the highest bitrate to the desired one, which results in computation cost. By leveraging the metadata extracted in the origin server, LwTE significantly reduces the transcoding time and computation costs.
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