Internet technologies have evolved over the past few years to enable more reliable data transmission and synchronization. As a result, the majority of video data shared today is transmitted through IP networks. This opens new possibilities for content production and delivery. Production has migrated from bespoke systems to generic IP networks using cloud computing and storage facilities. Moreover, compression of video content for streaming and delivery also benefits from cloud computing, general-purpose architectures, and distributed computing commodities. At the same time, the current developments in the area of Machine Learning (ML) and Artificial Intelligence (AI) led to an increased interest in the use of the associated tools to improve compression systems, reduce the encoding complexity, or design optimal streaming strategies. All of this opens the door to a variety of new opportunities and challenges for video production, compression, and delivery.
The goal of this Research Topic is to solicit original contributions both from academia and industry to solve new challenges and explore new opportunities arising from the use of new internet technology, cloud computing, and distributed computing and storage. As an example, cloud computing facilities provide general-purpose Graphics Unit Processors (GPUs) which can be used to parallelize video compression routines. These architectures also allow the execution of computing routines associated with AI and ML processing, enabling exciting new developments to streamline video production, compression, streaming and aid any application making use of them (e.g., a video conference system which summarizes the key segments of each session recorded to ease future browsing). Video coding tools may require a specific design to accommodate the constraints associated with these GPUs, and/or encoders may benefit from ad-hoc speed-ups and encoding tools to reduce the computational burden to data centers, or storage requirements in CDNs. In another example, content delivery via adaptive streaming may exploit parallel computing to accelerate the preparation of content and its transcoding. On the production side, content editing for post-production requires specific types of compression which minimize the transmission latency and enable random access throughout the video clip, etc. Finally, the massive use of cloud computing and data centers poses the problem of power consumption (e.g., when migrating data from different cloud data centers) and optimized use of processing resources, so that the whole system operates in an energy-efficient fashion.
Themes of interest in this Research Topic include but are not limited to the following:
• Compression, production, and streaming technologies devoted to meet low latency requirements and scale easily on distributed computing facilities
• Use of ML/AI methods to aid tasks such as video production, compression, storage, etc
• Compression systems exploiting cloud computing and/or distributed architectures
• Optimization of cloud resources in video compression applications
• Energy-aware video coding technology and/or energy efficient use of video processing resources
• Design and implementation of audio and video systems based on IP networks
• Design of quality metrics and assessment methodologies for IP-based production and distribution
• Design of metrics and/or methodologies to quantify the Quality of Experience (QoE) associated with distributed AV production and delivery systems
• Practical implementations of video codecs compliant with standards such as JPEG-XS, High-Throughput JPEG2000 (HTJ2K), Versatile Video Coding (VVC), etc
• Conversion of different AV compression formats using transcoding techniques
• Packaging standards, involving, but not limited to DASH and CMAF, including low latency variants
• Transmission standards, e.g., Route, MMTP, DVB-MABR
• Distribution network architectures, such as 5G or broadcast/broadband hybrid architectures
Topic Editor Matteo Naccari was/is employed by the company Audinate. Topic Editor Thomas Guionnet was/is employed by the company ATEME.
The remaining Topic Editors declare that they have no other commercial or financial relationships that could be construed as a potential conflict of interest.
Internet technologies have evolved over the past few years to enable more reliable data transmission and synchronization. As a result, the majority of video data shared today is transmitted through IP networks. This opens new possibilities for content production and delivery. Production has migrated from bespoke systems to generic IP networks using cloud computing and storage facilities. Moreover, compression of video content for streaming and delivery also benefits from cloud computing, general-purpose architectures, and distributed computing commodities. At the same time, the current developments in the area of Machine Learning (ML) and Artificial Intelligence (AI) led to an increased interest in the use of the associated tools to improve compression systems, reduce the encoding complexity, or design optimal streaming strategies. All of this opens the door to a variety of new opportunities and challenges for video production, compression, and delivery.
The goal of this Research Topic is to solicit original contributions both from academia and industry to solve new challenges and explore new opportunities arising from the use of new internet technology, cloud computing, and distributed computing and storage. As an example, cloud computing facilities provide general-purpose Graphics Unit Processors (GPUs) which can be used to parallelize video compression routines. These architectures also allow the execution of computing routines associated with AI and ML processing, enabling exciting new developments to streamline video production, compression, streaming and aid any application making use of them (e.g., a video conference system which summarizes the key segments of each session recorded to ease future browsing). Video coding tools may require a specific design to accommodate the constraints associated with these GPUs, and/or encoders may benefit from ad-hoc speed-ups and encoding tools to reduce the computational burden to data centers, or storage requirements in CDNs. In another example, content delivery via adaptive streaming may exploit parallel computing to accelerate the preparation of content and its transcoding. On the production side, content editing for post-production requires specific types of compression which minimize the transmission latency and enable random access throughout the video clip, etc. Finally, the massive use of cloud computing and data centers poses the problem of power consumption (e.g., when migrating data from different cloud data centers) and optimized use of processing resources, so that the whole system operates in an energy-efficient fashion.
Themes of interest in this Research Topic include but are not limited to the following:
• Compression, production, and streaming technologies devoted to meet low latency requirements and scale easily on distributed computing facilities
• Use of ML/AI methods to aid tasks such as video production, compression, storage, etc
• Compression systems exploiting cloud computing and/or distributed architectures
• Optimization of cloud resources in video compression applications
• Energy-aware video coding technology and/or energy efficient use of video processing resources
• Design and implementation of audio and video systems based on IP networks
• Design of quality metrics and assessment methodologies for IP-based production and distribution
• Design of metrics and/or methodologies to quantify the Quality of Experience (QoE) associated with distributed AV production and delivery systems
• Practical implementations of video codecs compliant with standards such as JPEG-XS, High-Throughput JPEG2000 (HTJ2K), Versatile Video Coding (VVC), etc
• Conversion of different AV compression formats using transcoding techniques
• Packaging standards, involving, but not limited to DASH and CMAF, including low latency variants
• Transmission standards, e.g., Route, MMTP, DVB-MABR
• Distribution network architectures, such as 5G or broadcast/broadband hybrid architectures
Topic Editor Matteo Naccari was/is employed by the company Audinate. Topic Editor Thomas Guionnet was/is employed by the company ATEME.
The remaining Topic Editors declare that they have no other commercial or financial relationships that could be construed as a potential conflict of interest.