User-Level Quality Assessment of a Delay-Aware Packet Dropping Scheme for VoIP

Álvarez-Flores, Erika P.; Ramos-Muñoz, Juan J.; Navarro-Ortiz, Jorge; Ameigeiras, Pablo; López-Soler, Juan M.

doi:10.5296/npa.v3i3.1113

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…For this purpose, Xu et al [8] provide Neighborhood Random Early Detection (NRED) scheme for ad-hoc network. To solve congestion problem and provide reliability to Voice over Internet Protocol (VoIP) service, Alvarez-Flores et al [34] designed a selected packet dropping algorithm for queue management policies called Drop-SwD. It sets parameters of dropping probabilities based on channel bandwidth usage of flows and provides fairness in TCP via early detection of congestion.…”

Section: Aqm-based Dropping Schemes In Connected Networkmentioning

confidence: 99%

“…Both states depend on the channel occupancy level of the node. To solve congestion problem and provide reliability to Voice over Internet Protocol (VoIP) service, Alvarez-Flores et al [34] designed a selected packet dropping algorithm for queue management policies called Drop-SwD. The proposed algorithm performs efficiently after dropping the lowest quality packets without disturbing the TCP and User Datagram Protocol traffic.…”

Section: Aqm-based Dropping Schemes In Connected Networkmentioning

confidence: 99%

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Bio‐inspired packet dropping for ad‐hoc social networks

Liaqat

Xia

Yang

et al. 2014

Int J Communication

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Ad-hoc social networks (ASNETs) explore social properties of nodes in communications. The usage of various social applications in a resource-scarce environment and the dynamic nature of the network create unnecessary congestion that might degrade the quality of service dramatically. Traditional approaches use drop-tail or random-early discard techniques to drop data packets from the intermediate node queue. Nonetheless, because of the unavailability of the social properties, these techniques are not suitable for ASNETs. In this paper, we propose a Bio-inspired packet dropping (BPD) algorithm for ASNETS. BPD imitates the matching procedure of receptors and epitopes in immune systems to detect congestions. The drop probability settings depend on the selection of data packets, which is based on node popularity level. BPD selects the most prioritized node through social properties, which is inspired by the B-cell stimulation in immune systems. To fairly prioritize data packets, two social properties are used: (1) similarity and (2) closeness centrality between nodes. Extensive simulations are carried out to evaluate and compare BPD to other existing schemes in terms of mean goodput, mean loss rate, throughput, delay, attained bandwidth, and overhead ratio. The results show that the proposed scheme outperforms these existing schemes.(QoS) and overhead cost. Therefore, to overcome scheduling problems, the consideration of socially aware properties provide high advantages in minimum QoS with less overhead cost [9]. Moreover, another reason for congestion in intermediate node 7 is the unavailability of receivers due to node mobility. Therefore, to avoid congestion-related losses in ASNETs, an accurate active queue management (AQM) policy is required [10]. The systemic constraints, such as poor link utilization and data packet drop rates in networks, result in delay queuing processes with increment of congested intermediate node. However, to solve the issue, some conflicts arise. In trying to reduce queuing delay, the size of buffer should be reduced, whereas it leads to low utilization of link at the same time, resulting in a high packet loss rate. To have an efficient queue management, accurate detection and dropping of targeted data packets are necessary because queue management strongly depends on dropping policies.Additionally, due to social behaviors of nodes in ASNET environments, dropping probabilities also depend on social properties of nodes. Primarily, there are multiple AQM schemes available to identify prioritization of data packets for dropping [11][12][13]. The non-prioritized data packets are dropped in an intermediate node due to less availability of the next node and resource constraint characteristics of nodes. Data packet dropping is broadly used to overcome issues present in the intermediate node, and it provides availability of prioritized data packets. However, every data packet dropping in an intermediate node is a kind of resource consumption. According to the concept of ASNET, dropped data packet...

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Section: Aqm-based Dropping Schemes In Connected Networkmentioning

confidence: 99%

Section: Aqm-based Dropping Schemes In Connected Networkmentioning

confidence: 99%

Bio‐inspired packet dropping for ad‐hoc social networks

Liaqat

Xia

Yang

et al. 2014

Int J Communication

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“…In addition, video files can be stored in physical supports as memory sticks or hard disk [1]. Although the transmission channels are getting better, it is still crucial to encode the videos to improve transmission quality and network performance [2] [3]. It is also important to define the best system to encode our videos (as a function of its features) for achieving the maximum optimization of encodings.…”

Section: Introductionmentioning

confidence: 99%

Choosing the best video compression codec depending on the recorded environment

Jiménez

Díaz

Sendra

et al. 2014

2014 IEEE Global Communications Conference

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In recordings of naturals environments there is often a predominant color due to the type environment such as a forest, a desert or ocean. Currently, there are a wide range of available video codecs that provide different compression results for different types of videos based on their predominant colors. Any way to save video flows bandwidth becomes a system to save or earn money. In this paper, we propose a decision algorithm which selects the most efficient codec to compress a video as a function of its chroma characteristics. In order to do this, first we created 5 videos where the main colors were white, black, red, green and blue in order to see the compression characteristics of each codec. Then, we used 4 videos of real environments with different predominant colors in order to test the video quality of the result and the compression time. Using these results we propose an algorithm that takes into account the predominant color and the video quality to efficiently select the best codec for each case. Moreover, we also provide the protocol design for the proper operation of our system.

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“…The authors in [27] analyze and define a delay-aware packet dropping scheme for Voice over IP (VoIP) by a user-level quality assessment. In the same direction, in [28] it is proposed the integration of machine learning techniques to adapt protocols for QoS-enabled distributed real-time applications.…”

Section: Related Workmentioning

confidence: 99%

Using Factor Analysis Techniques to Find Out Objective Video Quality Metrics for Live Video Streaming over Cloud Mobile Media Services

García-Pineda¹,

Felici-Castell²,

Segura-García³

2016

NPA

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The increased adoption of smartphones, the access to mobile broadband networks and the availability of public Clouds allow new multimedia services, called Cloud Mobile Media Services. Under this new architecture the proliferation of live video streaming applications and the Quality of Experience (QoE) given by the final user are an issue, due to the higher and variable delay, as result of the virtualization methods used in the Clouds. Thus in this paradigm new challenges appear related to keep and estimate a good QoE in terms of a standardized subjective video quality called Mean Opinion Score (MOS). In this paper, we propose several objective video quality metrics to estimate the subjective MOS based on Factor Analysis techniques both using Full Reference and Non Reference approaches. We compare the performance of the estimated MOS against publicly available video quality algorithms.

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User-Level Quality Assessment of a Delay-Aware Packet Dropping Scheme for VoIP

Cited by 5 publications

References 20 publications

Bio‐inspired packet dropping for ad‐hoc social networks

Bio‐inspired packet dropping for ad‐hoc social networks

Choosing the best video compression codec depending on the recorded environment

Using Factor Analysis Techniques to Find Out Objective Video Quality Metrics for Live Video Streaming over Cloud Mobile Media Services

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