Cloud-Native GPU-Enabled Architecture for Parallel Video Encoding

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.