S2VC: An SDN-based framework for maximizing QoE in SVC-based HTTP adaptive streaming

Tashtarian, Farzad; Varasteh, Amir

doi:10.1016/j.comnet.2018.09.007

Cited by 16 publications

(8 citation statements)

References 37 publications

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“…If the required computational resource is more than the available computational resource (Φ) at the edge server, we set the lower boundary of x to the first element that its value in culT ran is greater than d (lines 3-4). For example, let assume culT ran= [1,2,4,5,7,9] (the most popular is the first element), Φ=6, and d = 9 − 6 = 3. In this case, the 'FindElement' function returns the third element with the value of 4 as the lower boundary of x .…”

Section: E Heuristic Algorithmmentioning

confidence: 99%

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LwTE: Light-Weight Transcoding at the Edge

et al. 2021

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Due to the growing demand for video streaming services, providers have to deal with increasing resource requirements for increasingly heterogeneous environments. To mitigate this problem, many works have been proposed which aim to (i) improve cloud/edge caching efficiency, (ii) use computation power available in the cloud/edge for on-the-fly transcoding, and (iii) optimize the trade-off among various cost parameters, e.g., storage, computation, and bandwidth. In this paper, we propose LwTE, a novel Light-weight Transcoding approach at the Edge, in the context of HTTP Adaptive Streaming (HAS). During the encoding process of a video segment at the origin side, computationally intense search processes are going on. The main idea of LwTE is to store the optimal results of these search processes as metadata for each video bitrate and reuse them at the edge servers to reduce the required time and computational resources for on-thefly transcoding. LwTE enables us to store only the highest bitrate plus corresponding metadata (of very small size) for unpopular video segments/bitrates. In this way, in addition to the significant reduction in bandwidth and storage consumption, the required time for on-the-fly transcoding of a requested segment is remarkably decreased by utilizing its corresponding metadata; unnecessary search processes are avoided. Popular video segments/bitrates are being stored. We investigate our approach for Video-on-Demand (VoD) streaming services by optimizing storage and computation (transcoding) costs at the edge servers and then compare it to conventional methods (store all bitrates, partial transcoding). The results indicate that our approach reduces the transcoding time by at least 80% and decreases the aforementioned costs by 12% to 70% compared to the state-of-the-art approaches.INDEX TERMS Video streaming, transcoding, video on demand, edge computing.

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Section: E Heuristic Algorithmmentioning

confidence: 99%

“…By offering different bitrates of each segment, clients can choose the most appropriate bitrate according to the available network bandwidth. Many strategies have investigated the cost-effective delivery of video, such as on-the-fly transcoding in the network [4], [5], video caching [6], and hybrid approaches [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

LwTE: Light-Weight Transcoding at the Edge

et al. 2021

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“…These studies focus on transferring SVC layers over selected streaming paths and determining the suitable number of layers by taking UDP characteristics into account. The authors focus on providing QoE fairness among HAS clients, where different SVC layers can be downloaded from different servers in [55]. In [56], the SDN controller suggests the appropriate SVC layer for DASH clients to request by using a machine learning approach that takes network conditions as input.…”

Section: B Has Architectures Based On Sdn and Sandmentioning

confidence: 99%

SDN Assisted Codec, Path and Quality Selection for HTTP Adaptive Streaming

Kalan

Sayıt

2021

IEEE Access

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Adaptive streaming over HTTP is the dominant video streaming technology for more than a decade. HTTP Adaptive Streaming (HAS) systems provide a framework which enables clients to adapt quality with respect to network fluctuations during streaming, hence to optimize the perceived quality on the client side. Recently, network assistance is integrated with HAS in order to improve underlying network conditions and to provide network-related information to the clients. The performance of HAS systems can be further enhanced if the characteristics of the streamed video are considered. In this paper, we propose a HAS system architecture where Software Defined Networking (SDN) technology is utilized for assisting clients to select the most appropriate video codec and bitrate under the constraint of current network conditions as well as routing the video packet over the appropriate paths. In the proposed architecture, layered video is used, where each additional layer improves the quality. The controller estimates the packet loss probability by taking video codec characteristics, the bitrates of the layers and network capacity into account. Based on these estimations, the controller selects the appropriate codec type and video quality for the clients and manage the network. Simulation results show that the performance of the video streaming architecture can be improved significantly when codec, quality and path selection are jointly considered, and combined with SDN flexibility and advantageous.

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“…datos de un punto a otro, el jitter, la variación en un periodo de tiempo que tiene la latencia y la pérdida de paquetes ocasionada por fallas en la transmisión de paquetes IP de un nodo a otro. Los estudios de (Tashtarian et al, 2018;Wawge & Tijare, 2014) expanden estas investigaciones y correlacionan el nivel de los parámetros medidos con el nivel de satisfacción que se genera en el usuario en referencia (TIPHON, 2002) como se detalla en la tabla 1. La figura 2 muestra la separación en capas en una arquitectura SDN, como se explica en el trabajo de Barrientos, Rico, Coronel y Cuesta(2019) estas capas se comunican por medio de una interfaz de programación de aplicaciones API lo cual brinda a las nuevas redes emergentes una mayor flexibilidad, fiabilidad y seguridad.…”

Section: Métricas De Calidadunclassified

Aplicación de Balanceo De Carga Dinámico Para Servidores, Basada En Redes Definidas Por Software

Gilces

Villamar

2019

RISTI

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Resumen: Un aspecto relevante en investigación a nivel de redes de datos es la optimización de recursos. Las redes deben gestionar grandes volúmenes de tráfico sin introducir tiempos de espera innecesarios y garantizar la disponibilidad de servicios. Este artículo propone un algoritmo de balanceo de carga basado en un sistema de criterios combinados que utiliza tecnologías de redes definidas por software para obtener en tiempo de ejecución, parámetros desde distintos puntos de la red y seleccionar el servidor con las mejores condiciones para responder. La aplicación propuesta está escrita en lenguaje Python utilizando el controlador Ryu; la implementación de este programa en un switch, lo convierte en un balanceador de carga. Se utilizó la herramienta de simulación Mininet para la evaluación resultados; el experimento demuestra que el balanceador de carga basado en criterios combinados, reduce, hasta en un 50%, el tiempo de respuesta del servidor en comparación con el método tradicional Round Robin.Palabras-clave: SDN; balanceo de carga; redes; servidores; OpenFlow. Dynamic Load Balancing Application for Servers, Based on Software Defined Networking.Abstract: A relevant topic in current data network research is network resource optimization. Networks must cope with large volumes of traffic and ensure the availability of services without introducing unnecessary timeouts. This article presents a load balance algorithm based on software defined networks; it uses combined criteria to obtain at runtime, parameters from different nodes in the network to select the server with the best capability to deliver services. The proposed load balancer is written in Python language using the Ryu controller. The implementation of the python code on a switch allows it to act as a network load balancer. Mininet simulation tool was used in the experiment. Results demonstrate that the SDN load balancer obtains a response time up to 50% lower than Round Robin algorithm.

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S2VC: An SDN-based framework for maximizing QoE in SVC-based HTTP adaptive streaming

Cited by 16 publications

References 37 publications

LwTE: Light-Weight Transcoding at the Edge

LwTE: Light-Weight Transcoding at the Edge

SDN Assisted Codec, Path and Quality Selection for HTTP Adaptive Streaming

Aplicación de Balanceo De Carga Dinámico Para Servidores, Basada En Redes Definidas Por Software

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