Video streaming is looking a bit like magic. The uploader sends a video to one platform in one resolution and encoding format, while the viewer requests a video in a specific resolution and encoding format used by the device the video is streamed on. YouTube knows this best, as it represents the world's largest video platform with over 2 billion users visiting the platform each month. That takes a massive load on the server infrastructure over at Google's data centers that host the service. There is about 500 hours worth of video content uploaded to the platform every minute, and regular hardware isn't being enough anymore to handle everything.

That is why YouTube has developed custom chips, ASICs, that are called VCUs or Video (trans)Coding Units. In Google data centers, there is a large problem with transcoding. Each video needs to adapt to the streaming platform and desired specifications, and doing that on regular hardware is a problem. By using ASIC devices, such as VCUs, Google can keep up with the demand and deliver the best possible quality. Codenamed Argos, the chip can deliver 20-33x improvement in efficiency compared to the regular server platform. In data centers, the VCU is implemented as a regular PCIe card, with two chips under the heatsinks.

Xilinx, Inc., the leader in adaptive and intelligent computing, today introduced two real-time computing video appliances for easy-to-scale, ultra-high-density video transcoding applications. Based on the new Xilinx Real-Time (RT) Server reference architecture, these new appliances will enable service providers delivering applications such as eSports and game streaming platforms, social and video conferencing, live distance learning, telemedicine and live broadcast video to optimize video quality and bitrate at the lowest cost per channel for significant TCO savings over both software-based and fixed-architecture approaches.

Designed for edge and on-premise compute-intensive workloads where video channel density, throughput and latency are critical requirements, the new Xilinx Real-Time Video Appliances feature optimized hardware architectures and software to deliver the industry's highest channel density and lowest latency performance. The appliances are available in two pre-configured options integrating Xilinx Alveo data center accelerator cards - the High Channel Density Video Appliance and the Ultra-Low Bitrate Video Appliance.

In a release to its AIB partners, AMD listed out the key features of its high performance GPU in the Evergreen family, codenamed "Cypress". While not getting into the GPU specifications, it lists out the key technologies the GPUs support. It comes as no surprise that AMD will name Cypress-XT and Cypress-Pro as Radeon HD 5870 and Radeon HD 5850, a model number scheme that's been running for the past two generations. While product launches, tentatively on September 22, and (p)reviews keep us busy in September, retail availability can be expected only in October, just in time for that of Windows 7.

• 1GB GDDR5 memory
• ATI Eyefinity technology with support for up to three displays
• ATI Stream technology
• Designed for DirectCompute 5.0 and OpenCL
• Accelerated Video Transcoding (AVT)
• Compliant with DirectX 11 and earlier revisions,supports OpenGL 3.1
• ATI CrossFireX multi-GPU support for highly scalable performance
• ATI Avivo HD video and display technology
• Dynamic power management with ATI PowerPlay technology
• 2x DL-DVI, DisplayPort, HDMI
• PCI Express 2.0 interface

Today, at the NVISION 2008 conference, NVIDIA Corporation in conjunction with Pegasys Inc., makers of TMPGEnc 4.0 XPress multi-format video encoding software, showcased a technology demonstration to optimize video processing with the massively parallel architecture of the GPU.

Using NVIDIA CUDA technology (a C-like programming language programming for the GPU), Pegasys is taking advantage of the parallel processing capabilities of an NVIDIA GeForce GPU to create a GPU-enabled beta version of TMPGEnc 4.0 XPress software. The software is used to dramatically increase video decode and processing speed by as much as 446% on a GeForce GPU.