Abstract: In today's digital landscape, video streaming holds an important role in internet traffic, driven by the pervasive use of mobile devices and the surge in streaming platform popularity. In this work, we meticulously examine the energy consumption and CO₂ emissions of five popular open-source and fast video encoders: x264, x265, VVenC, libvpx-vp9, and SVT-AV1. These encoders are optimized software implementations of three video coding standards (AVC/H.264, HEVC/H.265, VVC/H.266) and two video formats (VP9 and AV1). To ensure a fair comparison, we also assess coding efficiency across these encoders at four distinct presets, applying three objective quality metrics. Additional factors like computing density and memory usage are considered. Our findings underscore that the x264 and SVT-AV1 encoders, especially at fast and faster presets, exhibit the lowest energy consumption and CO2 emissions. Notably, x264 boasts the most energy-efficient performance, yielding CO2 emissions of 0.28, 0.91, 2.07, and 9.74 g when encoding videos using faster, fast, medium, and slower presets, respectively. Furthermore, SVT-AV1 and VVenC encoders operating at a slower preset demonstrate superior coding efficiency, albeit at the cost of higher computational complexity and CO2 emissions of 60.5 g and 406 g, respectively. A salient observation from our study is that resolution and encoder presets serve as crucial parameters for curbing energy consumption and CO2 emissions, albeit with an inherent tradeoff in video quality.

Abstract: The estimation of energy consumption has become vital in developing eco-friendly and sustainable video streaming solutions to monitor CO2 emissions. In this work, we seek to evaluate and compare the energy consumption and CO2 emissions of the decoding process related to three popular video coding standards, namely AVC, HEVC, VVC, along with two video formats VP9, and AV1 through their real-time software decoders, including h264, hevc, VVdeC/OpenVVC, vp9, and libdav1d. The evaluation is conducted on two types of consumer hardware, desktop PC and laptop. To ensure a fair evaluation, we also assess the coding efficiency of software encoder implementations using three objective quality metrics. Additional factors like computing density and memory usage are considered. The experimental results revealed that the h264 decoder consumes the lowest energy and is associated with the lowest CO2 emissions compared to other decoders on both hardware platforms. On the other hand, the VVenC encoder enhances coding efficiency at the cost of increased decoding energy consumption and CO2 emissions, particularly noticeable in the case of the OpenVVC decoder. Meanwhile, x265/hevc achieves a compelling balance between coding efficiency and decoding energy consumption.

Abstract: Cloud infrastructure enables decentralized, on-demand service provision, allowing consumers to pay only for the resources they actually use. With consumers playing a critical role in the cloud ecosystem, any breach of the Service Level Agreement (SLA) between providers and consumers can have significant repercussions, often resulting in penalties for the provider. Data replication has emerged as a promising solution to address the dynamic demands and reliability challenges within the cloud environment. This paper introduces an innovative replication strategy that leverages data popularity to selectively replicate files, enhancing system-wide data availability, reducing query response times, and ensuring high-quality service levels. The strategy further adapts by dynamically determining the optimal number of replicas to add and identifying the best locations to store them. Experimental results validate the efficacy of this approach, demonstrating substantial improvements in performance and reliability.

Abstract: In any video surveillance system, it is very important to provide effective remote viewing to heterogeneous users with various network conditions and viewing device. To meet this adaptability requirement, the video transcoding process is inevitable, which consists of converting a video from one compressed format to another. However, since the transcoding operation is a lossy process, this can effect the performance of video analysis techniques such as visual object tracking. Consequently, in this work, we evaluate the impact of video transcoding parameters on the performance of visual object tracking algorithms. To achieve this, first, we build a new transcoding surveillance video (TSV) dataset in order to conduct a comprehensive benchmarking study of object tracking methods. Then, we design an evaluation framework for assessing the performance of ten state-of-the-art trackers on the TSV dataset. Finally, we analyze their performance regarding the different transcoding parameters. Experimental results show that the video transcoding parameters have a negative effect on the performance of object tracking methods. This effect is more pronounced for certain parameters, such as resolution, bitrate, and frame rate.