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https://doi.org/10.37815/rte.v34n3.935

Artículos originales

 

Evaluación y comparación de códecs de video para el despliegue de un sistema de comunicación resiliente

Evaluatio= n and comparison of video codecs for deploying a resilient communication system

 

Paúl Astudillo1 <= /span>https://orcid.org/0000-0= 001-8380-6103,

Christian Quind= e1  = https://orcid.org/0000-0= 002-9863-6941, Santiago González1 https://orcid.org/0000-0= 001-6604-889X, Iván Palacios1= https://orcid.org/0000-0= 002-3894-3341

 =

1Departamento de Eléctrica, Electrónica y Telecomunicaciones, Universidad de Cuenca, Cuenca, Ecuador

paul.astudillop@ucuenca.edu.ec, christian.quinde@ucuenca.edu.ec<= span style=3D'mso-bookmark:_Hlk61880979'>, santiago.gonzalezm@ucuenca.edu.ec, ivan.palacios@ucuenca.edu.ec 

 

Enviado:         2022/05/13

Aceptado:       2022/09/19

Publicado:      2022/11/30                         

Resumen

En escenarios de emergencia usualmente es difíc= il contar con una infraestructura de red, especialmente en lugares remotos o geográficamente complicados por el difícil acceso. Sin embargo, es necesario disponer de herramientas para comunicarse o realizar una valoración de la situación. En este sentido, las redes ad hoc surgen como una opción para solucionar esta problemática. En el presente artículo se propone el desarro= llo de un sistema de comunicación de emergencia para la transmisión de tráfico multimedia en un escenario real, entre los diferentes pisos de un edificio. Para tal objetivo, se realizaron múltiples experimentos para determinar los valores de métricas fundamentales (v.g. packet<= /span> loss, delay<= /span> y throughput) para la transmisión de audio= y video en tiempo real. En concreto, se desarrollaron herramientas de código abierto con capacidad de extraer las métricas requeridas y comparar de forma objetiva la calidad del video empleando mecanismos de codificación actuales (v.g. VP8, VP9, H.264, H.265). Los resultados obtenidos muestran que VP8 es= el códec más adecuado para el escenario propuesto. Además, se desarrolló una herramienta de videoconferencia con capacidad de adaptar las características del video (v.g. bitrate) acorde a las condiciones de la red. =

 

Sumario: Introducción, Materiales y Métodos, Resultados y Discusión y Conclusiones.

 <= /o:p>

Como citar:<= /span> Astudillo, P., Quinde, C., González, S. & Palacios, I. (20= 22). Evaluación y comparación de códecs de video para el despliegue de un sistema de comunicación resiliente. Revista Tecnológica - Espol, 34= (3), 12-30. http://www.rte.espol.e= du.ec/index.php/tecnologica/article/view/935


<= i>Palabras clav= e: Ad hoc, delay, packet loss, PSNR, redes resilie= ntes, throughput.

Abstract

In emergency scenarios, it is usually difficult to = rely on network infrastructure, especially in remote or geographically difficult-to-access locations. However, it is necessary to have tools to communicate or assess the situation. Therefore, ad hoc networks emerge as an option to solve this problem. This paper proposes the development of an emergency communication system for the transmission of multimedia traffic i= n a real scenario, between the different floors of a building. For this purpose, multiple experiments were carried out to determine the values of fundamental metrics (e.g., packet loss, delay, and throughput) for real-time audio and video transmission. Specifically, open-source tools were developed with the ability to extract the required metrics and objectively compare video quali= ty using current encoding mechanisms (e.g., VP8, VP9, H.264, H.265). The resul= ts obtained show that VP8 is the most suitable codec for the proposed scenario= . In addition, a videoconferencing tool was developed with the ability to adapt = the video characteristics (e.g., bitrate) according to the network conditions.<= o:p>

 

Keywords: Ad hoc, delay= , packet loss, PSNR, resilient network, throughput.=

 

Introducción

Una red resiliente hace referencia a la capacidad de la red para proporcionar y mantener un nivel de servicio aceptable frente a diversos fa= llos y retos en el funcionamiento, sea cual sea la naturaleza del reto al que se enfrenta (Frnda et al., 2016). En particula= r, los sistemas de comunicación son susceptibles a fallos y pérdida de conectivida= d, usualmente ocasionados por eventos naturales como inundaciones, terremotos, entre otros problemas derivados de la tecnología y actividades humanas mali= ciosas (Mauthe et al., 2016).

 

En consecuencia, cuando los servicios de red que forman parte de una infraestructura esencial (v.g. hospitales, aeropuertos, centrales eléctrica= s, entre otros) dejan de estar disponibles, se generan problemas de comunicaci= ón como resultado inevitable (Mauthe et al., 2016). Para solvent= ar este inconveniente se puede hacer uso de redes resilientes basadas en el paradigma denominado redes ad hoc, las cuales permiten la comunicación sin = la necesidad de un sistema central o algún tipo de infraestructura de red preexistente (Pan et al., 2019). Por ejemplo,= en (Salam et al., 2018) se propone un= a red ad hoc resiliente basada en drones con el propósito de mantener la comunica= ción durante la ocurrencia de catástrofes.

 

Cabe destacar que las redes ad hoc son redes descentralizadas y sin infraestructura, donde los nodos pueden operar como dispositivos terminales= y además como enrutadores para el reenvío de la información hacia dispositivos que estén dentro de su radio de cobertura (Mohammed et al., 2020). En este tipo= de redes, el comportamiento de métricas tales como el = delay o el throughput requieren de un análisis detallado. Por ejemplo, en (González et al., 2016) se realiza una evaluación del rendimiento del streaming= de video escalable en una red MANET (Mobile Ad hoc Networks), y se demuestra que a medida que aumenta el número de nodos, el throughput disminuye en el receptor. Además, en (Iliana et al., 2017) se establece = que el throughput= también disminuye dependiendo del número de dispositivos que ocupan el mismo canal inalámbrico.

 

De forma similar, en (Tolentino Medrano, 2021) se recalca qu= e la característica dinámica y la alta movilidad ocasionan pérdida de enlaces, lo que disminuye el throughput. Por otro lado, en (Ahmed et al., 2020), se demuestra= que el retardo de propagación (delay) de un enlace multisalto depende del número de repetidores y de la distancia entre repetidores adyacentes. En tal contexto= , en (Castellanos et al., 2015), se plantea u= na estrategia de transmisión adaptativa de video en función del ancho de banda disponible en la red, lo que permitiría reducir la congestión y por tanto l= a pérdida de paquetes.

En lo que respecta a situaciones de emergencia, es importante dotar a las personas afectadas y al personal de emergencia la capacidad de establec= er y utilizar rápidamente medios de comunicación independientes. De esta manera,= en (Jagannath et al., 2019) se presenta u= na solución completa de extremo a extremo que puede conectar a los supervivien= tes de una catástrofe entre sí y con las autoridades mediante una red ad hoc autosuficiente. De la misma forma, gracias a la movilidad de los vehículos aéreos no tripulados (UAVs), es posible crear u= na comunicación de red aérea para emergencias que apoye rápidamente y garantice las condiciones estrictas de tiempo en la búsqueda y rescate.

 

En (Dinh et al., 2019), se propone u= na arquitectura que apoya la comunicación entre los rescatistas y las víctimas mediante el uso de la red aérea ad hoc para emergencias. Además, la mayor p= arte de las infraestructuras están dañadas o completamente destruidas en los escenarios posteriores a las catástrofes, por ello, se puede utilizar una r= ed ad hoc vehicular (VANET) para llevar a cabo la operación de rescate, ya que= no requiere ninguna infraestructura preexistente. En este contexto, en (Khaliq et al., 2019) se plantea y = valida una forma eficaz de transmitir la información crucial mediante el desarroll= o de una aplicación y el despliegue de un testbed experimental en un entorno vehicular.

 

Por otro lado, la vigilancia del tráfico es un aspecto importante pa= ra los Sistemas Inteligentes de Transporte (ITS) que ayudan a detectar inciden= tes automáticamente. En estos casos, las redes ad-hoc podrían ser una opción vi= able y de bajo coste, no obstante la transmisión de v= ideo manteniendo un nivel adecuado de calidad de servicio, es un desafío. Por tal motivo, (Felici-castell et al., 2021)  presentan una técnica de transmisión de= vídeo adaptativo en tiempo real para transportar flujos de vídeo desde las cámara= s a los servidores externos de monitorización del tráfico.

 

Por otra parte, la gran popularidad de las aplicaciones multimedia, ha impulsado un rápido crecimiento del con= sumo de contenidos de alta resolución, incrementando de forma dramática la deman= da de ancho de banda. En tal sentido, el uso de códecs resulta fundamental ya = que permiten comprimir los datos, lo que contribuye a reducir la carga de tráfi= co en la red, el almacenamiento requerido y por tanto mejorar la velocidad de trasferencia. Cabe indicar que, el desarrollo de códecs representa una líne= a activa de investigación y en constante evolución (Rao et al., 2020). Entre los estándares más usados se tiene: H264, H265, VP8 y VP9 (Bienik et al., 2016).

 

En lo que respecta al análisis de códecs, en (Paredes et al., 2016) se indica que= para aplicaciones en VANET’s (Vehicular Ad hoc Networks) el estándar H.265/HEVC es superior en compresión a los estánd= ares H.264/AVC y VP9. En cuanto a velocidad de codificación, en (ZhangTicao & MaoShiwen, 2019) se señala que H264/AVC es más rápido que H.265/HEVC y VP9, no obstante presenta la mitad = de eficiencia de codificación.

 

Además, en (Grois et al., 2013) se recalca qu= e VP9 puede presentar un tiempo de codificación de hasta 100 veces el tiempo de H.264, lo que ocasiona un retraso considerable en la emisión de un video. E= n (Sharma et al., 2019) se presenta un estudio donde se indica que los nuevos códecs (H.265 y VP9) proporcionan = una mejora de la tasa de bits de más del 50% respecto a los anteriores H.264 y = VP8, pero son considerablemente más lentos en el proceso de codificación.

 

Así mismo, en (Barman & Martini, 2017), se establece= que el rendimiento real de los códecs de video depende en gran medida de factor= es tales como, el tiempo de codificación, el tipo de contenido (videos natural= es/de animación/video sintético), la complejidad del video, la resolución, los parámetros de codificación (v.g. GoP, fps, VBR/CBR) y las métricas de evaluación de la cali= dad (objetivo/subjetivo).

 

Por último, en cuanto al consumo de CPU, tanto VP9 como H.265 implementan más etapas durante la compresión que H.264, lo que aumenta la c= arga sobre los recursos computacionales. Sin embargo, se puede utilizar codifica= ción basada en hardware para disminuir el consumo o en su defecto aumenta= r el número de CPU empleados. Además, el estudio descrito en (Mengzhe et al., 2015), demuestra qu= e al aumentar el número de CPU el tiempo de codificación disminuye considerablemente, aunque como consecuencia se tiene una disminución del PS= NR (Peak Signal to Noise Ratio).=

 

En tal contexto, en este artículo se presenta la evaluación experime= ntal de un sistema de comunicación multimedia robusto frente a eventos de emergencia. La propuesta se basa en herramientas hardware/software de tipo open source. En particular, se pres= enta un desarrollo a nivel de capa de aplicación, el mismo que permite la transmisión de tráfico multimedia (audio/video) con capacidad de adaptarse a las condiciones de la red, para lo cual se analizó previamente el mejor mecanismo de compresión considerando las características de la red empleada= .

 

Cabe indicar que el estudio fue realizado sobre un escenario real consistente en el edificio matriz de la Empresa Eléctrica Regional Centro S= ur (Cuenca-Ecuador). Dicho escenario cuenta con una red ad hoc multi-salto, la misma que fue desplegada previamente con el fin de monitorizar el comportamiento dinámico del edificio (Palacios et al., 2022). Donde, la principal contribución de este artículo es el desarrollo de una metodología para caracterizar una red resiliente para comunicaciones de emergencia, y la implementación de una aplicación de videollamada con capacidades de video adaptativo sobre dispositivos con bajo procesamiento utilizando el códec VP= 8.

 

El resto del artículo se encuentra organizado de la siguiente forma.= En la sección Materiales y Métodos, se presenta el procedimiento definido para caracterizar la red ad hoc, la metodología para evaluar la calidad del video empleando diferentes códecs, así como el desarrollo de una herramienta capa= z de transmitir audio y video en tiempo real con capacidad de adaptarse a las ca= racterísticas de la red. Posteriormente, en la siguiente sección se discuten los resultad= os obtenidos. Finalmente, se exponen las principales conclusiones del estudio.=

 

Figura = 1=

     Metodología = para la evaluación de la propuesta=

 

 

 

Materiales y Mét= odos

En la <= !--[if supportFields]><= /span> REF _Ref114571574 \h Figura 1 se presenta la metodología empleada para= la evaluación de la propuesta, la misma consta de cuatro etapas. En primer lug= ar, se desarrollaron un conjunto de experimentos con la finalidad de caracteriz= ar la red ad hoc disponible en el sitio de estudio. A continuación, se procedi= ó a analizar de forma subjetiva y objetiva la calidad del tráfico de video, en = la red, dicho proceso abarcó desde la selección de un conjunto de videos hasta= la comparación y análisis del códec más adecuado acorde a las características = de la red.

 

Posteriormente= , se procedió a transmitir audio y video en tiempo real para verificar la comunicación en casos de emergencia, con tal finalidad se desarrolló una solución a nivel de capa de aplicación. Para concluir, se procedió a evalua= r de forma objetiva el comportamiento del tráfico multimedia transmitido en térm= inos de packet loss, delay y throughput.

 

Caracterización de la red ad hoc

Antes de trans= mitir el video es necesario verificar la capacidad de la red, mediante un análisis de métricas tales como el packet loss,= el delay y e= l ancho de banda disponible. En tal sentido, se realizaron cuatro experimentos dond= e se ubicó un ordenador fijo en el sótano y otro ordenador en un piso superior (= piso uno, tres, cinco y siete), tal como se esquematiza en la Figura 2. Como se puede apreciar, el experimento desarrollado entre el sótano y el piso 1 genera tres saltos (Figura 2= a), el experimento con respecto al piso 3 genera cuatro saltos, el experimento en el piso 5 genera cinco saltos y finalmente el experimento entre el ordenador del sótano y el piso 7 genera = seis saltos (Figura 2= b). Por otro lado, cada experimento consi= stió en generar tráfico constante y transmitirlo desde un ordenador hacia el otr= o, aumentando el = bitrate desde los 100Kbps hasta los 10Mbps para de esta forma determinar el umbral = del throughput en cada piso.

 

Metodología para la evaluación subjeti= va y objetiva de la calidad de video

Para determina= r el códec más adecuado para la transmisión de video sobre la red, se realizaron múltiples experimentos. En particular, se definió una metodología, la misma= que se esquematiza en la Figura 3. Como se aprecia, el primer paso fue seleccionar videos en formato CIF (Common Intermediate Format),= el mismo que es ampliamente usado en aplicaciones de videoconferencia. En tal sentido, se escogieron y descargaron las secuencias foreman, mother-daughter y <= i>news, disponibles en (National Science Foundation, 2000), las mismas que presentan diferentes condiciones como, por ejemplo, cambios rápidos y lentos de escena, diferente cantidad de objetos presentes y estabilidad o no de la cámara.

 

Posteriormente= , se codificaron los videos empleando como parámetro principal el bitrate y como parámetros adicionales el GoP (Group of Pictures) y el framerate. En este punto se consideró el umbral determinado en la caracterización de la red ad hoc. El tercer paso consisti= ó en realizar una valoración subjetiva de la calidad del video con los diferentes parámetros de codificación. Para esta evaluación se aplicó el método Double Stimulus= Impairment Scale (DSI= S), una descripción detalla de esta metodología se presenta en (Zhang &= amp; Bull, 2014). El cuarto paso fue definir de forma sub= jetiva la mejor opción, realizando una media entre las valoraciones de las personas encuestadas.

 

A continuación, utilizando la herramienta GStreamer (GStreamer: Open Source Multimedia Fram= ework, 2022), se codificaron y transmitieron en tiempo real los videos sobre el propio ordenador, donde se capturaron todos los paquetes generados para obt= ener el perfil del tráfico de cada uno. En el sexto paso se realizó una valoraci= ón del perfil del tráfico vs. el umbral de la red ad hoc, para determinar si es posible la transmisión del video sobre la red existente. A partir del análi= sis, en caso de ser necesario, se ajustan nuevamente las métricas y se regresa al paso 2. El siguiente paso tiene como objetivo definir los parámetros de bitrate, = GoP y framerate que proporcionen los mejores resultados buscan= do un equilibrio entre calidad y compresión.

 

Figura = 2=

Ubicación de los ordenador= es para los experimentos

 

a)

b)      =  Experimento en el piso 7 (6 saltos)= .

 

En cuanto al paso número 8, se codificaro= n y transmitieron los videos en tiempo real con los códecs: H.264, H.265, VP8 y VP9. Además, se evaluaron las métricas de throughput, delay, packet loss, carga de CPU, tiempo de codificación y PSNR. Después, se realizaron los experimentos entre los diferentes pisos del edificio con los distintos videos seleccionados y se compararon los resultados. Finalmente, se definió el códec con mejores resultados, el cual se emplea posteriormente para la transmisión de video e= n tiempo real. Cabe resaltar que para el desarrollo de los experimentos se implement= aron herramientas software, las mismas que han sido liberadas para su uso= y se encuentran disponibles en https://github.com/christianquinde/GUI y https://github.com/ch= ristianquinde/GUI-rx.

 

Figura = 3=

Metodología para la selección del= mejor códec de video

Figura = 4=

Diagrama secuencial para la herra= mienta de audio y video en tiempo real

 

Transmisión de audio y video en tiempo= real

En esta fase d= e la metodología, se desarrolló una herramienta para la transmisión de audio y v= ideo en tiempo real, basada en el protocolo RTP (Real Time Transport Protocol). En la Figura 4 se presenta un diagrama secuencial que describe las principales características y funcionalidades implementadas. C= abe destacar que al realizar la llamada se mide el ancho de banda de la red, lo cual permite codificar el video con diferentes valores de bitrate, y por lo tanto se consigue una funcionalidad de vídeo adaptativo. En la Figura 5, se puede observar los resultados del vi= deo adaptativo, donde se evidencia que la calidad del video con 350kbps es supe= rior al video con 250kbps, sin embargo incrementa el consumo de ancho de banda. Adicionalmente, la aplicación desarrollada ha si= do liberada para su uso y se encuentra disponible en el repositorio de Github en https://github.com/christian= quinde/App-videoconferencia.

 

Figura = 5=

Video codificado con 350 kbps (izq) y 250 kbps (der)

<= span style=3D'mso-ansi-language:ES-EC'>

 

Cálculo de métricas durante la transmi= sión de audio y video en tiempo real

Para el cálcul= o del delay y d= e la pérdida de paquetes se empleó la información de control proporcionada por el protocolo RTCP, en concreto los paquetes Sender Rep= ort (SR) y Receiver Report (RR), los mismos = que se intercambian entre los dos terminales que realizan la videollamada de emergencia. El delay se calcula utilizando la Ecuación 1, donde A es la hora en que se recibe el= bloque RR. Last = Sender Report (LSR) es el campo con la marca de tiempo d= el último SR recibido, y Delay since Last Sender Report (DLSR) es el tiempo entre la recepción del último paquete SR y el envío del paquete RR. Cabe mencionar que, aunque los enlaces pueden tener retrasos asimétricos, este método resulta adecuado como una medida aproximada del retardo entre los dos extremos.=

 

                                                =                                                              (1)

 

Por otra parte= , para obtener el porcentaje de packet loss = se utilizó el campo fraction lost de los paquetes RR y el campo cumulative = number of packets lost del último RR recibido. El primer campo representa la fracción de paquetes RTP perdidos desde que se envió el anter= ior paquete SR. Dicha fracción se define como el número de paquetes perdidos dividido para el número de paquetes esperados (256), el cual está definido = por 8 bits dentro del campo fraction lost del paquete RTCP. El segundo campo utiliz     ado contiene el número total de paquetes perdidos des= de el inicio de la transmisión. Con estos valores y con el número de paquetes RR capturados se obtiene el packet loss = mediante la Ecuación 2.

 

                                                =                                                 (2)

 

Por otro lado,= para obtener el throughput de la transmisión,= ya sea de audio o video, se empleó la herramienta tcpstat= con un intervalo de 0.1 segundos. Es decir, a partir del tráfico capturado = se realiza un filtrado por puertos para obtener el perfil del tráfico y el throughput promedio correspondiente a cada flujo. Cabe mencionar que se realizó un promedio de 10 transmisiones y una comparación de cada métrica por piso. De forma similar, las herramientas desarrolladas han sido liberadas para su us= o y pueden descargarse en https://github.com/christian= quinde/herramienta-medicion y https://github.com/christianquinde/server= .

&n= bsp;

Resultados y Dis= cusión

Caracterización de la red ad hoc

Con el objetivo de caracterizar la red ad= hoc, se realizaron varios experimentos transmitiendo tráfico constante entre los diferentes pisos del edificio. Para cada tasa de transmisión se repitió el experimento un total de 10 veces con el objetivo de efectuar un análisis estadístico. Los resultados obtenidos se presentan en la Figura 6, donde se evidencia que conforme se incrementa el número de saltos disminuye el throughput de la red. Además, se puede observar que el throughput máximo alcanzado es de 3.5 Mbps y el mínimo de 1.6 Mbps para los pisos 1 (3 saltos) y 7 (6 saltos) respectivamente. Cabe resaltar que a partir de los resultados obtenidos se estableció el umbral d= e la red con el valor de 1.6 Mbps.

 

 

 

 

 

 

 

Figura 6

Throughput [Kbps] vs data rate deseado [= Mbps]

 

Figura 7=

Retardo vs número de piso

 

Figura 8=

Pérdida de paquetes vs número de piso

 

Análisis del delay y packet = loss

Para obtener el retardo de la red entre l= os ordenadores (es decir entre los nodos extremo), se realizaron múltiples experimentos transmitiendo mensajes tipo Internet Control Message Protocol (ICM= P), de los cuales se obtuvo el número de paquetes perdidos y el tiempo requerido p= ara cada uno. Los resultados que se presentan en la Figura 7 demuestran que el retardo aumenta conforme se incrementa el número de salto= s, siendo 14.02 ms y 5.47 ms para el piso 7 y 1 respectivamente.

Por otro lado, para obtener el porcentaje= de packet loss = se realizó un promedio de las transmisiones realizadas. En la  REF _Ref114573385 \h Figura 8 se presentan los valores obtenidos para cada piso, donde se observa que existe= una mayor pérdida de paquetes en los pisos más altos. Los resultados indican qu= e el piso 7 tiene menor pérdida de paquetes que el piso 5, esto se debe a que ex= iste variabilidad debido a que se trata de un escenario real y se debe tener presente la naturaleza del medio inalámbrico. Por lo cual, a partir de los intervalos de confianza se tienen resultados similares para los pisos 5 y 7= .

 

Evaluación subjetiva y objetiva de la calidad de video

Siguiendo el esquema de la = Figura 3, la valoración subjetiva de la calidad de video se determinó mediante la métrica MOS (Mean Opinion Score), los resultados= se presentan en la Tabla 1. Además, se caracterizó el perfil del tráfico de las secuencias de video para compararlo frente al valor umbral del throughput obtenido en la red (1.6 Mbps).

 

Tabla 1=

Mejores resultados del análisis subjetivo con bitrate = =3D 350 kbps

VIDEO

GoP

Fps

Foreman


30


20, 25

Mother-daughter

News<= o:p>

Foreman


60


20, 25

Mother-daughter

News<= o:p>

 

En este contexto, para los tres videos seleccionados se escogieron las métricas que aseguran una mayor calidad y fluidez del video, es decir un tamaño de GoP ig= ual a 30 y un valor de 25 fps (<= i>Frames per Second), teniendo en cuenta que en la m= ayoría de casos no se superó el umbral establecido de l= a red ad hoc.

 

Evaluación del throu= ghput

Con el objetivo de analizar el throughput se realizaron ci= nco transmisiones con cada códec de video en todos los pisos. En la Figura 9= se presentan los resultados obtenidos para el caso más crítico, es decir cu= ando se requiere un mayor número de saltos (piso 7). Donde se observa que VP9 requiere mayor ancho de banda. Sin embargo, el throughput máximo está por = debajo del umbral establecido en la caracterización de la red ad hoc, por lo que el video se reproduce sin problemas.

 

Además, en la Figura 1= 0, se presentan los perfiles del tráfico de una transmisión del video “News” con = cada códec en el piso 7. Como era de esperarse, el perfil del tráfico es variabl= e y presenta ráfagas, las mismas que al superar el umbral de la red generan pér= dida de paquetes. Con lo cual, los mejores resultados se obtuvieron con los códe= cs VP8 y H265, seguidos de H264 y por último VP9.

 

 

 

 

 

 

 

 

 

Figura 9=

Throughput promedio en el piso 7

 

Figura 10

Throughput para el video “News” en el piso 7

 

 

 

Figura 11

Delay pr= omedio obtenido para el video “News” en cada piso

 

Evaluación del retardo<= /span>

Para determinar el valor del delay se = restan los tiempos entre el transmisor y receptor con ayuda del protocolo RTCP y un servidor NTP que sincroniza a las dos terminales. Se puede notar que al sub= ir de piso el del= ay se incrementa. Cabe recalcar que los tiempos de delay varían dependiendo de= la hora del día (nivel de interferencia) y el nivel de precisión que tiene el cliente al sincronizarse con el servidor NTP.

 

En la Figura 1= 1, se presentan los resultados para el video “News” y se observa que en el caso m= ás extremo (seis saltos), la aplicación cumple con los requisitos establecidos= en la recomendación UIT-T G.114 (Tsbmail, 2003), la cual recomienda un retardo menor a 150ms para una transmisión en tiempo rea= l.

 

Evaluación de la pérdida de paquetes

La siguiente métrica evaluada es el porce= ntaje de pérdida de paquetes. Para obtener este valor se compara el número de paquetes generados en el transmisor con los paquetes obtenidos en el recept= or empleando la Ecuación 3. Para que la comparación sea adecuada se realiza un filtrado de los paquetes capturados por dirección IP de origen y destino, número de puerto y protocolo de transporte (UDP, User Datagram Protocol).

 

En la Figura 1= 2,= se presentan los resultados obtenidos par= a el video “News” en cada piso. Donde, a partir= de la gran amplitud de las barras de error se nota que existe una gran variabilid= ad en los resultados entre cada una de las transmisiones, esto se debe a las interferencias y las condiciones propias del canal inalámbrico. Sin embargo= , se observa que en promedio, la pérdida de paquetes es baja y no supera el 5 %, estando en la mayoría de casos por debajo del 1 %, lo cual se considera una pérdida de paquetes “aceptable” para una llamada de voz y video (Vyopta, 2019).

 

        =        (3)

Figura 12

Porcentaje de packet loss para= el video “News” en cada piso

 

Evaluación del consumo de CPU<= /span>

Para obtener el consumo de CPU, en cada transmisión se empleó la herramienta top de Linux. Los resultados se presentan en la Figura 13, donde se puede destacar una tendencia similar en todos los pisos para el caso del video mother-daughter, siendo el códec V= P8 el más eficiente, ya que cuenta con el menor consumo de CPU. En cuanto a los códecs VP9 y H264 tienen un consumo equitativo alrededor del 100 %, y por último H265 es el códec con mayor consumo de CPU. Cabe destacar que el coma= ndo top muestra el consumo como un porcentaje de una sola CPU.

 =

En los sistemas multinúcleo, se pueden te= ner porcentajes superiores al 100 %, como es el caso de H265. Es decir, el 100% representa un solo núcleo funcionando a su máxima capacidad. Cabe indicar q= ue, se fijó el uso de CPU de VP8 y VP9 en 2 y 4 núcleos respectivamente para po= der realizar una transmisión en tiempo real y ajustarse al tiempo de duración d= el video. Los códecs H264 y H265 utilizan el preset speed-preset en su valor por defecto (Me= dium) que mantiene un equilibrio entre calidad y velocidad de codificación.<= /o:p>

 

Evaluación del tiempo de codificación y transmisión

Para esta métrica, al modificar los valor= es por defecto de los códecs, se establece que el tiempo de codificación y tiempo = de transmisión sean iguales al tiempo de video para poder realizar una transmi= sión en tiempo real.

 

Análisis del PSNR

En la Figura 14 se muestran los resultados de la métrica PSNR para cada video, dependiendo del tipo de códec. Respecto a los resultados, se evidencia H265 como el códec c= on mejor calidad debido a que tiene un mayor valor de PSNR, a éste le siguen H= 264 y VP9 manteniendo resultados similares y por último está el códec VP8.=

 

 

Figura 13

Consumo de CPU promedio para el video “News” en cada piso

 

Figura 14

Valor de PSNR de los videos

 

Definición del códec más adecuado

Basándose en el análisis realizado, en la= Figura 15 se presenta un resumen de los mejores resultados para cada códec. En tal senti= do se seleccionó a VP8 como el códec más adecuado para la aplicación requerida. Concretamente porque en términos de throughput es el códec que no presenta ráfagas que super= en el umbral establecido y esto permite que la pérdida de paquetes sea menor. = A su vez, es el códec que demanda menos CPU, permitiendo hacer uso de equipos con menos recursos de hardware. Finalmente, en términos de PSNR tiene resultados similares a los demás códecs.

 

 

 

 

 

 

Figura 15

Desempeño de cada códec por métrica

 

Resultados de la transmisión de audio y video en tiempo real<= /p>

Para transmitir audio y video entre los d= iferentes pisos del edificio, primero se estableció un umbral de 1.6 Mbps para realiz= ar diferentes codificaciones con distintos bitrates. Debido al estado de la red, en algunos instant= es al codificar con 350 kbps se generaron ráfagas que superan los 1.6 Mbps, oc= asionando gran pérdida de paquetes y por lo tanto una degradación de la calidad. A pa= rtir de lo observado, cuando la red disponía de un throu= ghput por encima del umbral el video se codificó con un bitrate igual a 350 kbps y = por debajo de este umbral se codificó con 250 kbps. De esta manera se obtiene siempr= e una videollamada fluida.

 

En la Tabla 2, se presenta el ancho de banda medido antes de iniciar la transmisión en cada p= iso y también el b= itrate seleccionado automáticamente en cada uno, teniendo en cuenta el umbral establecido. Donde se observa que el piso más crítico (piso 7) fue el único= en codificar el video con 250 kbps ya que en ese instante hubo sobrecarga en la red.

 

Tabla 2=

Ancho de banda medido por piso para la selección del bitrate= adecuado

PISO

SALTOS

ANCHO DE BANDA <= span lang=3DEN-US style=3D'font-size:10.0pt;line-height:102%'>[Mbps]

BITRATE [Kbps]

1

3

2.82<= o:p>

350

3

4

2.72<= o:p>

350

5

5

2.5

350

7

6

1.59<= o:p>

250

 

Para evaluar objetivamente la calidad de = audio y video, se calcularon las métricas a partir del tráfico capturado en tiempo real una vez establecida la videollamada. Cabe indicar que se realizó un to= tal de 10 capturas con una duración de dos minutos cada una con el objetivo de efectuar un análisis estadístico de los resultados. Al finalizar las transmisiones en todos los pisos, se promediaron las tres métricas (throughput, delay, packet loss)= tanto para audio como para video.

 

Además, en la Figura 16 correspondiente al delay, se comprobó que, al incrementar el número de saltos, el delay aumen= tó tanto para el audio como para el video. Finalmente, para la <= !--[if supportFields]> REF _Ref115170962 \h Figura 1= 7 correspondiente al packet loss se verificó que el piso 7 tie= ne mayor pérdida de paquetes. Cabe recalcar que el packet loss y el delay son dependientes de la sobrecarga o interferencia que existe en ese momento en la red.<= /span>

 

 =

=  

Figura 16

Resultados para la métrica delay de audio y video del usua= rio A y B

Figura 17

Resultados para la métrica packet los= s de audio y video del usuario A y B

 

Conclusiones

La caracterización de la red ad hoc demos= tró que, al existir interferencias, las cuales aumentan o disminuyen dependiend= o de la hora del día al variar el número de redes wifi que afectan al canal 7, y= conforme se incrementa el número de saltos, la red se sobrecarga reduciendo el ancho= de banda disponible. Debido a esto, es de gran importancia contar con un siste= ma de transmisión de video capaz de comprobar el estado de la red en el momento que se desea realizar la videollamada y escoger el bitrate más adecuado en ese instante.

 

Además, permitió establecer el umbral máx= imo de ancho de banda en 1.6 Mbps en el piso más crítico, siendo este la referencia para mantener una comunicación aceptable. Cabe mencionar que la metodología empleada consistió en colocar nodos fijos dentro de un área limitada del edificio y para obtener el mayor ancho de banda se eligió un intervalo del = día con la menor interferencia.

 

En cuanto a los experimentos realizados p= ara obtener el códec óptimo, se comprobó que VP8 permite la transmisión de un m= ismo video con ráfagas y con una amplitud menor al umbral de la red, alrededor de 1Mbps, mientras que los demás códecs tuvieron ráfagas que superan los 1.6Mb= ps. Otro aspecto importante es que con VP8 se requirió tan solo un máximo dos <= span class=3DSpellE>CPUs, a diferencia de VP9 que utilizó 4 y tanto H264 = como H265 utilizaron 8. Siendo estas las razones principales para descartar a los demás códecs estudiados. De esta forma, en caso de emergencia, cualquier persona puede hacer uso de la herramienta de videollamada incluso con dispositivos limitados en hardware. 

 =

Cabe destacar que, la aplicación de video= de emergencia desarrollada permite una comunicación bidireccional de audio y v= ideo entre dos usuarios dentro de la red ad hoc y además con la capacidad de adaptarse a las condiciones de la red para garantizar una videoconferencia fluida.  <= /p>

 =

Por último, las pruebas realizadas confir= maron el correcto funcionamiento y establecimiento de una videollamada entre los pisos más alejados, en donde se presentan las condiciones más críticas en cuanto a = delay, throughput y packet loss.= Con lo cual, se estableció un bitrate de 250 Kbps para obtener una videollamada fluida en el piso más crítico, generando un d= elay promedio de 50ms y un packet loss = promedio de 5.68% en uno de los usuarios. En conclusión, por medio de la herramienta desarrollada se estableció una comunicación de emergencia en tiempo real aprovechando una red ad hoc desplegada. 

 

Como futuras investigaciones se plantea a= plicar el sistema a una red con nodos móviles y un protocolo de enrutamiento dinám= ico para cubrir un área mayor dentro del edificio. Además, se puede utilizar un método donde el canal usado sea escogido de forma dinámica para utilizar el mayor ancho de banda disponible.

 

Reconocimientos

Los autores de= este trabajo agradecen al Vicerrectorado de Investigación de la Universidad de Cuenca y al proyecto de investigación “Tecnologías IoT= y Redes Inalámbricas de Sensores Aplicados a la Monitorización de Salud Estructural en Edificios Esenciales de la Ciudad de Cuenca” que permitieron realizar el trabajo de investigación haciendo uso de su red.

 

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6

Paúl Astudillo, Christian Quinde, Santiago González, Iván Palacios

5

Evaluación y comparación = de códecs de video para el despliegue de un sistema de comunicación resili= ente

 

E= scuela Superior Politécnica del Litoral, ESPOL

<= /p>

 

R= evista Tecnológica Espol – RTE Vol. 34, N° 3 (Noviembre, 2022) / e-ISSN 1390-= 3659

<= /p>

 

E= scuela Superior Politécnica del Litoral, ESPOL

<= /p>

 

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