A STUDY ON CHANNEL AND DELAY-BASED SCHEDULING ALGORITHMS FOR LIVE VIDEO STREAMING IN THE FIFTH GENERATION LONG TERM EVOLUTION-ADVANCED

This paper investigates the performance of a number of channel and delay-based scheduling algorithms for an efficient QoS (Quality of Service) provision with more live video streaming users over the Fifth Generation Long-Term Evolution-Advanced (5G LTE-A) network. These algorithms were developed for use in legacy wireless networks and minor changes were made to enable these algorithms to perform packet scheduling in the downlink 5G LTE-A. The efficacies of the EXP and M-LWDF algorithms in maximizing the number of live video streaming users at the desired transmission reliability, minimizing the average network delay and maximizing network throughput, are shown via simulations. As the M-LWDF has a simpler mathematical equation as compared to the EXP, it is more favoured for implementation in the complex downlink 5G LTE-A. ABSTRAK: Kertas ini menyiasat prestasi sebilangan saluran dan algoritma penjadualan berdasarkan kelewatan untuk penyediaan QoS (Kualiti Perkhidmatan) yang cekap dengan banyak pengguna video secara langsung melalui rangkaian Generasi Kelima Long-Term Evolution Advanced (5G LTE-A). Algoritma-algoritma yang disiasat di dalam kertas ini dicadangkan untuk digunakan dalam generasi rangkaian tanpa wayar yang lama dan sedikit perubahan dibuat untuk membolehkan algoritma ini menyokong penjadualan paket dalam downlink 5G LTE-A. Keberkesanan EXP dan M-LWDF algoritma dalam memaksimumkan jumlah pengguna pada kebolehpercayaan transmisi yang diinginkan dari streaming video secara langsung, meminimumkan kelewatan rangkaian, dan memaksimumkan truput rangkaian ditunjukkan melalui simulasi. Namun, dengan M-LWDF mempunyai formula matematik yang mudah dibandingkan dengan EXP, ia lebih sesuai untuk digunakan dalam downlink 5G LTE-A yang lebih kompleks.

Caracterización de tráfico para el servicio de Video Streaming en vivo sobre DASH en redes 4G basado en analizadores sintácticos

Contexto: El tráfico de datos móviles generado por los servicios de video aumenta a diario. Para enfrentar dicha situación, los proveedores de servicios de telecomunicaciones deben conocer el comportamiento del tráfico de video y así ajustar los recursos de la red que permitan satisfacer y mantener los niveles de calidad requeridos por los usuarios. Los estudios de caracterización de tráfico en redes 4G para el servicio Live Video Streaming (LVS) son escasos y los disponibles son obtenidos a partir de escenarios de simulación en los cuales no se consideran las condiciones reales de funcionamiento de este tipo de redes. Método: Este trabajo se centra en encontrar un modelado que caracterice el tráfico a partir de las funciones de densidad de probabilidad del servicio LVS bajo la técnica de streaming adaptativo DASH en redes LTE. Las trazas analizadas para realizar el estudio del modelado fueron adquiridas en escenarios reales de emulación considerando las condiciones de funcionamiento frecuentemente presentadas en la prestación real del servicio, para lo cual se definieron cinco escenarios de prueba. Resultados: Se presenta la descripción, a partir de la parametrización de algunas funciones de densidad de probabilidad encontradas, de diferentes modelos de tráfico del servicio bajo estudio y para cada uno de los escenarios de prueba preestablecidos en una red 4G-LTE. Conclusiones: A partir de los resultados, se concluye que el modelo de tráfico depende de las condiciones de cada escenario y que no existe un modelo único que describa el comportamiento general del servicio LVS bajo la técnica de streaming adaptativo DASH en una la red LTE emulada.

A Novel Model for Large-Scale Online College Learning in Postpandemic Era: AI-Driven Approach

COVID-19 is a pandemic with a wide reach and explosive magnitude, and the world has been bracing itself for impact. Many have lost their jobs and savings, and many are homeless. For better or worse, COVID-19 has permanently changed our lives. For college students, the pandemic means giving up most of the on-campus experience in the postpandemic era and performing online learning instead. Virtual lessons may become a permanent part of college education. Large-scale online learning typically utilizes interactive live video streaming. In this study, we analyzed a codec and video streaming transmission protocol using artificial intelligence. First, we studied an intraframe prediction optimization algorithm for the H.266 codec based on long short-term memory networks. In terms of video streaming transmission protocols, real-time communication optimization based on Quick UDP Internet connections and Luby Transform codes is proposed to improve the quality of interactive live video streaming. Experimental results demonstrate that the proposed strategy outperforms three benchmarks in terms of video streaming quality, video streaming latency, and average throughput.

Abstract 11939: Can Live Video Streaming From Bystander's Smartphone Improve the Quality of Chest Compressions in Real Out-of-Hospital Cardiac Arrest?

Introduction: Good quality of cardiopulmonary resuscitation (CPR) provided by bystanders is important for the outcome in out-of-hospital cardiac arrest (OHCA). A live video stream from the bystander’s smartphone to the medical dispatcher might improve the quality of chest compressions performed during CPR. Methods: At the Copenhagen Emergency Medical Services in Denmark, the medical dispatcher can add a live video to the emergency call. In case of OHCA, the medical dispatcher guides bystanders in dispatcher-assisted CPR (DA-CPR). After initiating chest compressions, the medical dispatcher can add live video streaming. A cohort study was conducted with an evaluation of performed chest compressions from the video footage before and after the dispatcher used the video to instruct CPR (video-instructed DA-CPR). Correct chest compressions were defined according to European Resuscitation Council Guidelines. Results: CPR was provided with a live video stream in 52 OHCA calls, in which 90 bystanders performed chest compressions. Thirty OHCA occurred at a public location, and more than four bystanders were present in 32 (62%) cases. In 26 cases, chest compressions were performed by more than one bystander. Eight (9%) bystanders performed correct chest compressions before video-instructed DA-CPR. For the bystanders first initiating insufficient CPR improvements were observed for: hand placement 58% (n=17/29), compressions rate 73% (n=17/21), and compressions depth 62% (n=19/31) following video-instructed DA-CPR. For the second bystander providing CPR (n=26) improvements were still observed for: hand placement 57% (n=4/7), compressions rate 73% (N=8/11), and compressions depth 53% (n=11/21) following video-instructed DA-CPR. For the third and fourth bystander (n=10), providing CPR improvements were seen for: hand placement 100% (n=2/2), compressions rate 50 % (n=2/4), and compressions depth 60% (n=3/5). Eighteen bystanders had a chest compressions performance measurement that could not be observed. Conclusions: A live video from the bystander`s smartphone to the medical dispatcher could improve the quality of chest compressions in CPR, and guidance seems important not just for the first bystander but for all bystanders performing CPR.