QoE-SDN APP: A Rate-guided QoE-aware SDN-APP for HTTP Adaptive Video Streaming

Liotou, Eirini; Samdanis, Konstantinos; Pateromichelakis, Emmanouil; Passas, Nikos; Merakos, Lazaros

doi:10.1109/jsac.2018.2815421

Cited by 43 publications

(18 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another point to consider is that, since a centralized-control approach-which combines information otherwise segregated (i.e., user, network, service, and application)-might be seen challenging to implement, there are already authors who presented feasible solutions. For instance, Liotou et al [41] proposed a solution that allows effective communication between Video Service Providers (VSPs) and mobile network operators' (MNOs), which enables feedback towards a better network-aware video segment selection.…”

Section: Discussionmentioning

confidence: 99%

SAND/3: SDN-Assisted Novel QoE Control Method for Dynamic Adaptive Streaming over HTTP/3

et al. 2019

View full text Add to dashboard Cite

Dynamic Adaptive Streaming over HTTP (DASH) is a widely used standard for video content delivery. Video traffic, most of which is generated from mobile devices, is shortly to become the most significant part of Internet traffic. Current DASH solutions only consider either client-or server-side optimization, leaving other components in DASH (e.g., at the transport layer) to default solutions that cause a performance bottleneck. In that regard, although it is assumed that HTTP must be necessarily transported on top of TCP, with the latest introduction of HTTP/3, it is time to re-evaluate its effects on DASH. The most substantial change in HTTP/3 is having Quick UDP Internet Connections (QUIC) as its primary underlying transport protocol. However, little is still know about the effects on standard DASH client-based adaption algorithms when exposed to the future HTTP/3. In this paper, we present SAND/3, an SDN (Software Defined Networking)-based Quality of Experience (QoE) control method for DASH over HTTP/3. Since the official deployment of HTTP/3 has not been released yet, we used the current implementation of Google QUIC. Preliminary results show that, by applying SAND/3, which combines information from different layers orchestrated by SDN to select the best QoE, we can obtain steadier media throughput, reduce the number of quality shifts in at least 40%, increase the amount downloaded content at least 20%, and minimize video interruptions compared to the current implementations regardless of the client adaption algorithm.

show abstract

Section: Discussionmentioning

confidence: 99%

SAND/3: SDN-Assisted Novel QoE Control Method for Dynamic Adaptive Streaming over HTTP/3

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In [51], an SND-based MEC framework is proposed and implemented, which provides scalability to PGW and SGW entities depending on the number of user equipment. A programmable QoE-SDN application is introduced in [52] to serve the customers of video streaming providers. This application allows video streaming providers to control the desired QoE by a direct communication with the mobile network operator.…”

Section: B Sdn In Mec 1) Sdn Definitionmentioning

confidence: 99%

Multi-Access Edge Computing: A Survey

et al. 2020

View full text Add to dashboard Cite

Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this paper, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

show abstract

“…Generally speaking, SLAW could be used for modeling the pedestrian movement behaviour in small scale social gathering events, such as train stations, malls, and theme parks due to its accuracy and realistic outlook in terms of network traffic per square meter in comparison with the random mobility models [150], [151], [152]. However, there have been a number of efforts on providing a more realistic mobility model for applications such as theme parks, airports, fairs, and festivals with prohibited or restricted use of vehicles.…”

Section: ) Self-similar Least Action Walk Model (Slaw) [70]mentioning

confidence: 99%

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

et al. 2020

View full text Add to dashboard Cite

With the widespread use of IoT applications and the increasing trend in the number of connected smart devices, the concept of routing has become very challenging. In this regard, the IPv6 Routing Protocol for Low-power and Lossy Networks (PRL) was standardized to be adopted in IoT networks. Nevertheless, while mobile IoT domains have gained significant popularity in recent years, since RPL was fundamentally designed for stationary IoT applications, it could not well adjust with the dynamic fluctuations in mobile applications. While there have been a number of studies on tuning RPL for mobile IoT applications, but still there is a high demand for more efforts to reach a standard version of this protocol for such applications. Accordingly, in this survey, we try to conduct a precise and comprehensive experimental study on the impact of various mobility models on the performance of a mobility-aware RPL to help this process. In this regard, a complete and scrutinized survey of the mobility models has been presented to be able to fairly justify and compare the outcome results. A significant set of evaluations has been conducted via precise IoT simulation tools to monitor and compare the performance of the network and its IoT devices in mobile RPL-based IoT applications under the presence of different mobility models from different perspectives including power consumption, reliability, latency, and control packet overhead. This will pave the way for researchers in both academia and industry to be able to compare the impact of various mobility models on the functionality of RPL, and consequently to design and implement application-specific and even a standard version of this protocol, which is capable of being employed in mobile IoT applications.

show abstract

QoE-SDN APP: A Rate-guided QoE-aware SDN-APP for HTTP Adaptive Video Streaming

Cited by 43 publications

References 35 publications

SAND/3: SDN-Assisted Novel QoE Control Method for Dynamic Adaptive Streaming over HTTP/3

SAND/3: SDN-Assisted Novel QoE Control Method for Dynamic Adaptive Streaming over HTTP/3

Multi-Access Edge Computing: A Survey

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

Contact Info

Product

Resources

About