End-to-end and network-internal measurements of real-time traffic to residential users

Ellis, Martin; Perkins, Colin; Pezaros, Dimitrios P.

doi:10.1145/1943552.1943567

Cited by 12 publications

(25 citation statements)

References 13 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The characteristics of the packet loss on these networks has been found to be quite different from uniform random loss [4], so we expect FEC performance to be different too. Since previous work has not looked at FEC performance using data from streaming to residential broadband networks, this is where we focus.…”

Section: Related Workmentioning

confidence: 95%

“…We use the dataset described in [4], which contains endto-end measurements of streaming synthetic IPTV-like traffic across the open Internet at a range of bit-rates (1-8.5Mb/s) to residential ADSL and Cable users. The dataset consists of over 3800 traces, varying between one and ten minutes in duration (between 6000 and 120000 packets per trace).…”

Section: Methodsmentioning

confidence: 99%

“…The FEC schemes are 2D parity codes (2D) [1], Reed-Solomon erasure codes (RSE) [2], and LDPCStaircase codes (LDPC) [3]. We use packet loss measurements from video streams sent from a well-connected server to residential users [4] to inform trace-driven simulations of FEC performance, studying residual packet loss rates and fraction of lost packets successfully recovered. Our contributions are 1) a simulation-based evaluation of AL-FEC performance on real networks, using traces of unmanaged Internet streaming to residential users (i.e., reflecting the conditions that interdomain IPTV flows could experience if they were deployed in future; 2) an explanation of the differences between our results and those of previous evaluations of these schemes under random packet loss (particularly the effect of bursty packet loss); and 3) guidelines for use of FEC, to recommend which FEC schemes and parameters work well under the loss conditions common on residential networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance analysis of AL-FEC for RTP-based streaming video traffic to residential users

Ellis

Pezaros

Perkins

2012

2012 19th International Packet Video Workshop (PV)

Self Cite

View full text Add to dashboard Cite

Abstract-Real-time applications used by residential customers, such as streaming video and IPTV, are sensitive to packet losses, whether due to IP-layer congestion, or link-layer problems such as bit errors induced by impulse noise. To achieve acceptable user experience for these applications, numerous applicationlayer forward error correction (AL-FEC) schemes have been proposed. We evaluate some of the FEC schemes developed as part of the OpenFEC project, using packet loss traces of IPTVlike traffic measured on ADSL and Cable links. We consider the effectiveness of these schemes in correcting the loss patterns present on residential links, explain why performance is different using measured loss traces compared with previous simulations using uniform random packet loss, and give recommendations for the use of FEC in streaming video applications deployed to residential Internet users.

show abstract

Section: Related Workmentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance analysis of AL-FEC for RTP-based streaming video traffic to residential users

Ellis

Pezaros

Perkins

2012

2012 19th International Packet Video Workshop (PV)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Representative examples include end to end measurement of Realtime Transport Protocol (RTP) traffic using traceroute [18], video packet flow statistics extracted from tcpdump data [38] and application QoS metrics for video buffering time [30] derived from TCP traffic data.…”

Section: Related Workmentioning

confidence: 99%

Scalable classification of QoS for real-time interactive applications from IP traffic measurements

Middleton

Modafferi

2016

Computer Networks

View full text Add to dashboard Cite

Measurement of network Quality of Service (QoS) has attracted considerable research effort over the last two decades. The recent trend towards Internet Service Providers (ISP's) offering application-specific QoS is creating possibilities for more sophisticated QoS metrics to be offered by ISP's in service level agreements. This in turn could be used for the purposes of improved network optimization and billing according to application specific QoS guarantees. We report a scalable near real-time approach using passively logging IP traffic data for classification of application latency and packet loss across a range of real-time interactive applications. We run six experiments involving Minecraft, Quake 3 Urban Terror, VLC video streaming and the commercial Wirofon VOIP application. We use a mixture of laboratory and realworld deployments, with run times ranging from hours to days, and observe a combination of real and simulated ISP latency and packet loss events. Our binary classification (i.e. classes 'OK' or 'lag') 10-fold cross validation F1 scores are between 0.80 and 0.93 depending on application type. Our multi-class classification (i.e. classes representing discrete packet loss or latency ranges) 10-fold cross validation F1 scores for Minecraft are 0.89 for latency and 0.90 for packet loss. With new business models between ISP's and application developers being actively considered this work represents a significant contribution to the debate by providing scientific evidence relating to a novel approach to scalable QoS measurement

show abstract

“…Trace repositories are available for researchers to simulate different environments [35], [36]. Traces typically include information about lost packets and packet arrival time, the latter is particularly useful to simulate different playout deadlines applied at the receiver by discarding packets that would be too late for decoding.…”

Section: Transmission Influencementioning

confidence: 99%

Objective Video Quality Assessment — Towards Large Scale Video Database Enhanced Model Development

Barkowsky

Masala

Wallendael

et al. 2015

IEICE Trans. Commun.

View full text Add to dashboard Cite

SUMMARYThe current development of video quality assessment algorithms suffers from the lack of available video sequences for training, verification and validation to determine and enhance the algorithm's application scope. The Joint Effort Group of the Video Quality Experts Group (VQEG-JEG) is currently driving efforts towards the creation of large scale, reproducible, and easy to use databases. These databases will contain bitstreams of recent video encoders (H.264, H.265), packet loss impairment patterns and impaired bitstreams, pre-parsed bitstream information into files in XML syntax, and well-known objective video quality measurement outputs. The database is continuously updated and enlarged using reproducible processing chains. Currently, more than 70,000 sequences are available for statistical analysis of video quality measurement algorithms. New research questions are posed as the database is designed to verify and validate models on a very large scale, testing and validating various scopes of applications, while subjective assessment has to be limited to a comparably small subset of the database. Special focus is given on the principles guiding the database development, and some results are given to illustrate the practical usefulness of such a database with respect to the detailed new research questions. key words: video quality assessment, large scale database, reproducible research

show abstract

End-to-end and network-internal measurements of real-time traffic to residential users

Cited by 12 publications

References 13 publications

Performance analysis of AL-FEC for RTP-based streaming video traffic to residential users

Performance analysis of AL-FEC for RTP-based streaming video traffic to residential users

Scalable classification of QoS for real-time interactive applications from IP traffic measurements

Objective Video Quality Assessment — Towards Large Scale Video Database Enhanced Model Development

Contact Info

Product

Resources

About