Abstract
Multimedia streaming has become an essential aspect of contemporary life and the ever-growing demand for high-quality streaming has fostered the development of new video codecs and improvements in content delivery. Cloud computing, particularly cloud architectures, has played a pivotal role in this evolution, offering dynamic resource allocation, parallel execution, and automatic scaling-critical features for HTTP Adaptive Streaming applications. This paper presents two specialized containers designed for video encoding (using two implementations of H264: x264 that encodes in the CPU and H264 NVENC that also uses the GPU). These containers are deployed on a Kubernetes cluster with four GPUs. The experiments focus on the performance and resource consumption of the encoder containers under different Kubernetes cluster and replica configurations. The best setup shows a 12.7% reduction in encoding time for x264 and a 15.98% for H264 NVENC compared to the other configurations considered. Besides, the encoding time of H264 NVENC is reduced by a 3.29 factor compared to x264. To test the behavior in realistic scenarios, four videos were encoded at five different resolutions. The mean encoding time per segment is reduced by a 3.75 factor when using H264 NVENC compared to x264. These results hold significant implications for live streaming applications, particularly for low-latency use cases.
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Notes
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NVIDIA Video Codec SDK, available at: https://developer.nvidia.com/video-codec-sdk.
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- 4.
See for instance https://support.google.com/youtube/answer/2853702.
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Salcedo-Navarro, A., Peña-Ortiz, R., Claver, J.M., Garcia-Pineda, M., Gutiérrez-Aguado, J. (2024). Cloud-Native GPU-Enabled Architecture for Parallel Video Encoding. In: Carretero, J., Shende, S., Garcia-Blas, J., Brandic, I., Olcoz, K., Schreiber, M. (eds) Euro-Par 2024: Parallel Processing. Euro-Par 2024. Lecture Notes in Computer Science, vol 14803. Springer, Cham. https://doi.org/10.1007/978-3-031-69583-4_23
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