ABSTRACT
During the COVID-19 pandemic around the world, it is difficult to visit a doctor at the hospital. Many hospitals are trying to adapt to develop telemedicine systems to serve their patients. Hence the origin of this research as well is to develop a Telemedicine system to use Websocket and WebRTC have been applied to support video calling functionality in hopes of creating sessions to serve clients efficiently. This paper shows the results of the tests obtained when used in real-world applications on the internet which found that the results have been used network utility very efficiently. It averaged 1.86 Mbps for down link and averaged 1.66 Mbps for uplink bitrate, but still had high memory consumption. When compared to video calling active in the video conferencing system is still considered a waste of resources at 79% more than other systems. © 2023 IEEE.
ABSTRACT
Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure. In addition, the recent COVID-19 pandemic fueled a surge in the use of video conferencing tools. Since videos take up considerable bandwidth ( ∼ 100 Kbps to a few Mbps), improved video compression can have a substantial impact on network performance for live and pre-recorded content, providing broader access to multimedia content worldwide. We present a novel video compression pipeline, called Txt2Vid, which dramatically reduces data transmission rates by compressing webcam videos ('talking-head videos') to a text transcript. The text is transmitted and decoded into a realistic reconstruction of the original video using recent advances in deep learning based voice cloning and lip syncing models. Our generative pipeline achieves two to three orders of magnitude reduction in the bitrate as compared to the standard audio-video codecs (encoders-decoders), while maintaining equivalent Quality-of-Experience based on a subjective evaluation by users ( n=242 ) in an online study. The Txt2Vid framework opens up the potential for creating novel applications such as enabling audio-video communication during poor internet connectivity, or in remote terrains with limited bandwidth. The code for this work is available at https://github.com/tpulkit/txt2vid.git. © 1983-2012 IEEE.
ABSTRACT
In the recent year, people had to work from home due to the outbreak of the COVID-19 pandemic. When the majority of the family members are working online, the bitrate experienced by the average user may drop, especially if some members have to work in rooms that suffer from weak coverage. Benefiting from the emerging concept of Reflective Intelligent Surfaces (RIS), the network coverage in our houses can be greatly improved. This paper presents a study of an RIS-assisted system for an indoor scenario operating at 2.4GHz. We propose an RIS placement approach that is based on minimizing the pathloss of the channel, to enhance the rate of bad coverage rooms, while taking into consideration their user occupancies. The proposed approach, which we refer to as the Weighted RIS Placement, is modeled and simulated for a single RIS. The problem is then extended to a two-RIS scenario. Our results show that the Weighted RIS placement provides significant rate gains. Also, this is the first work that models the communication channels for the individual rooms, using corresponding Rician K-factor values that reflect the indoor layout. © 2021 IEEE.