4 research outputs found
A Survey on Energy Consumption and Environmental Impact of Video Streaming
Climate change challenges require a notable decrease in worldwide greenhouse
gas (GHG) emissions across technology sectors. Digital technologies, especially
video streaming, accounting for most Internet traffic, make no exception. Video
streaming demand increases with remote working, multimedia communication
services (e.g., WhatsApp, Skype), video streaming content (e.g., YouTube,
Netflix), video resolution (4K/8K, 50 fps/60 fps), and multi-view video, making
energy consumption and environmental footprint critical. This survey
contributes to a better understanding of sustainable and efficient video
streaming technologies by providing insights into the state-of-the-art and
potential future directions for researchers, developers, and engineers, service
providers, hosting platforms, and consumers. We widen this survey's focus on
content provisioning and content consumption based on the observation that
continuously active network equipment underneath video streaming consumes
substantial energy independent of the transmitted data type. We propose a
taxonomy of factors that affect the energy consumption in video streaming, such
as encoding schemes, resource requirements, storage, content retrieval,
decoding, and display. We identify notable weaknesses in video streaming that
require further research for improved energy efficiency: (1) fixed bitrate
ladders in HTTP live streaming; (2) inefficient hardware utilization of
existing video players; (3) lack of comprehensive open energy measurement
dataset covering various device types and coding parameters for reproducible
research
Parametrien etsintä HEVC:n tehokkaalle moodivalinnalle
High Efficiency Video Coding (HEVC) is the latest video coding standard. It halves the achieved bit rate compared with the previous standard, Advanced Video Coding (AVC). However, the bit rate decrease comes with 40% increase in encoding complexity. This is mainly due to larger number of block coding modes, including Symmetric motion partitions (SMPs), Asymmetric motion partitions (AMPs), and larger coding units of up to 64x64 pixels. These new features are mainly used for Inter prediction that accounts for 60-70% of the whole encoding time. For this reason, optimization of Inter prediction is the main topic in this Thesis.
To tackle the Inter prediction complexity, a parametric exploration was chosen as the approach. The exploration was done by gradually shifting the focus from the most coarse optimization to the parameter fine tuning. The selected approach in this study required thousands of individual tests so an automated solution was needed. This led to the creation of a new software solution, TUT Task Manager. It is capable of automatically distributing the tasks of parametric exploration to any number of nodes available in the local network. In total, TUT Task Manager was used to run 4000 tests with a combined CPU time of 14 months.
The results were used to create a set of recommended schemes for Inter mode selection. Overall, these new schemes are shown to provide 31-50% complexity saving against the default configuration of HM 11.0, with a minor bit rate increase of 0.2-1.3%. They also provide better RDC performance than the existing solutions. The tools and methods used in this work are so generic that they can be used to further optimize other parts of the video codec
Image and Video Coding Techniques for Ultra-low Latency
The next generation of wireless networks fosters the adoption of latency-critical applications such as XR, connected industry, or autonomous driving. This survey gathers implementation aspects of different image and video coding schemes and discusses their tradeoffs. Standardized video coding technologies such as HEVC or VVC provide a high compression ratio, but their enormous complexity sets the scene for alternative approaches like still image, mezzanine, or texture compression in scenarios with tight resource or latency constraints. Regardless of the coding scheme, we found inter-device memory transfers and the lack of sub-frame coding as limitations of current full-system and software-programmable implementations.publishedVersionPeer reviewe
Kvazaar HEVC Still Image Coding on Raspberry Pi 2 for Low-cost Remote Surveillance
This demonstrator serves as a proof-of-concept of our multi-camera remote surveillance system that supports 1080p still image capture with a 10-second refresh rate. Image capture, compression, and broadcast are implemented in each Raspberry Pi 2 camera node. Image compression is conducted with an open-source Kvazaar HEVC encoder that outputs HEVC images in BPG format. The BPG images are broadcast from camera nodes to terminals over the Internet through WebSocket protocol. The images can be played back with most Web browsers in remote locations with Internet access.acceptedVersionPeer reviewe