Characterizing and Improving the Reliability of Broadband Internet Access

In this paper, we empirically demonstrate the growing importance of reliability by measuring its effect on user behavior. We present an approach for broadband reliability characterization using data collected by many emerging national initiatives to study broadband and apply it to the data gathered by the Federal Communications Commission's Measuring Broadband America project. Motivated by our findings, we present the design, implementation, and evaluation of a practical approach for improving the reliability of broadband Internet access with multihoming.Comment: 15 pages, 14 figures, 6 table

Performance Evaluation of IPTV over WiMAX Networks Under Different Terrain Environments

Deployment Video on Demand (VoD) over the next generation (WiMAX) has become one of the intense interest subjects in the research these days, and is expected to be the main revenue generators in the near future. In this paper, the performance of Quilty of Service of video streaming (IPTV) over fixed mobile WiMax network is investigated under different terrain environments, namely Free Space, Outdoor to Indoor and Pedestrian. OPNET is used to investigate the performance of VoD over WiMAX. Our findings analyzing different network statistics such as packet lost, path loss, delay, network throughput.Comment: arXiv admin note: substantial text overlap with arXiv:1302.1409, and substantial text overlap with other internet sources by other author

No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences

Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives

A two-level Markov model for packet loss in UDP/IP-based real-time video applications targeting residential users

The packet loss characteristics of Internet paths that include residential broadband links are not well understood, and there are no good models for their behaviour. This compli- cates the design of real-time video applications targeting home users, since it is difficult to choose appropriate error correction and concealment algorithms without a good model for the types of loss observed. Using measurements of residential broadband networks in the UK and Finland, we show that existing models for packet loss, such as the Gilbert model and simple hidden Markov models, do not effectively model the loss patterns seen in this environment. We present a new two-level Markov model for packet loss that can more accurately describe the characteristics of these links, and quantify the effectiveness of this model. We demonstrate that our new packet loss model allows for improved application design, by using it to model the performance of forward error correction on such links

Deep Room Recognition Using Inaudible Echos

Recent years have seen the increasing need of location awareness by mobile applications. This paper presents a room-level indoor localization approach based on the measured room's echos in response to a two-millisecond single-tone inaudible chirp emitted by a smartphone's loudspeaker. Different from other acoustics-based room recognition systems that record full-spectrum audio for up to ten seconds, our approach records audio in a narrow inaudible band for 0.1 seconds only to preserve the user's privacy. However, the short-time and narrowband audio signal carries limited information about the room's characteristics, presenting challenges to accurate room recognition. This paper applies deep learning to effectively capture the subtle fingerprints in the rooms' acoustic responses. Our extensive experiments show that a two-layer convolutional neural network fed with the spectrogram of the inaudible echos achieve the best performance, compared with alternative designs using other raw data formats and deep models. Based on this result, we design a RoomRecognize cloud service and its mobile client library that enable the mobile application developers to readily implement the room recognition functionality without resorting to any existing infrastructures and add-on hardware. Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and 89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in a quiet museum, and 15 spots in a crowded museum, respectively. Compared with the state-of-the-art approaches based on support vector machine, RoomRecognize significantly improves the Pareto frontier of recognition accuracy versus robustness against interfering sounds (e.g., ambient music).Comment: 29 page

VIQID: a no-reference bit stream-based visual quality impairment detector

In order to ensure adequate quality towards the end users at all time, video service providers are getting more interested in monitoring their video streams. Objective video quality metrics provide a means of measuring (audio)visual quality in an automated manner. Unfortunately, most of the current existing metrics cannot be used for real-time monitoring due to their dependencies on the original video sequence. In this paper we present a new objective video quality metric which classifies packet loss as visible or invisible based on information extracted solely from the captured encoded H.264/AVC video bit stream. Our results show that the visibility of packet loss can be predicted with a high accuracy, without the need for deep packet inspection. This enables service providers to monitor quality in real-time

Comparing objective visual quality impairment detection in 2D and 3D video sequences

The skill level of teleoperator plays a key role in the telerobotic operation. However, plenty of experiments are required to evaluate the skill level in a conventional assessment. In this paper, a novel brain-based method of skill assessment is introduced, and the relationship between the teleoperator's brain states and skill level is first investigated based on a kernel canonical correlation analysis (KCCA) method. The skill of teleoperator (SoT) is defined by a statistic method using the cumulative probability function (CDF). Five indicators are extracted from the electroencephalo-graph (EEG) of the teleoperator to represent the brain states during the telerobotic operation. By using the KCCA algorithm in modeling the relationship between the SoT and the brain states, the correlation has been proved. During the telerobotic operation, the skill level of teleoperator can be well predicted through the brain states. © 2013 IEEE.Link_to_subscribed_fulltex