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.
This presentation is motivated by the idea of leveraging a novel Light-weight Transcoding approach at the Edge (LwTE), 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. 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.
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.
About Farzad Tashtarian
Farzad Tashtarian received his Ph.D. degree in computer engineering from the Ferdowsi University of Mashhad, Mashhad, Iran. He is currently a Postdoctoral Researcher in the ATHENA project with the Institute of Information Technology (ITEC), Alpen-Adria-Universität Klagenfurt (AAU). Before joining the team, he was an Assistant Professor with the Azad University of Mashhad. His current research interests include end-to-end latency and QoE in video streaming, video networking, software-defined networking, network function virtualization, mathematical modeling, and distributed optimization.
This event will be conducted in English