Quality of experience ties together user perception, experience, and expectations to application and network performance, typically expressed by quality of service parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. Against this background, this article proposes a generic formula in which QoE and QoS parameters are connected through an exponential relationship, called IQX hypothesis. The formula relates changes of QoE with respect to QoS to the current level of QoE, is simple to match, and its limit behaviors are straightforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores is expressed as functions of loss and reordering ratio, the latter of which is caused by jitter. For web surfing as the second application area, matchings provided by the IQX hypothesis are shown to outperform previously published logarithmic functions. We conclude that the IQX hypothesis is a strong candidate to be taken into account when deriving relationships between QoE and QoS parameters.

/pdf/a-generic-quantitative-relationship-between-quality-of-3btyt2c2i5.pdf

A generic quantitative relationship between quality of experience and quality of service

VoIP: A comprehensive survey on a promising technology

The speech encoding apparatus (220) has a first layer encoding section (2201), a first layer decoding section (2202), a delay section (2203), a subtracting section (104), a frequency domain transforming section (101), a second layer encoding section (105) and a multiplexing section (106).

Encoding device, decoding device, and method thereof

Voice over IP (VoIP) has become a popular protocol for making phone calls over the Internet. Due to the potential transit of sensitive conversations over untrusted network infrastructure, it is well understood that the contents of a VoIP session should be encrypted. However, we demonstrate that current cryptographic techniques do not provide adequate protection when the underlying audio is encoded using bandwidth-saving Variable Bit Rate (VBR) coders. Explicitly, we use the length of encrypted VoIP packets to tackle the challenging task of identifying the language of the conversation. Our empirical analysis of 2,066 native speakers of 21 different languages shows that a substantial amount of information can be discerned from encrypted VoIP traffic. For instance, our 21-way classifier achieves 66% accuracy, almost a 14-fold improvement over random guessing. For 14 of the 21 languages, the accuracy is greater than 90%. We achieve an overall binary classification (e.g., "Is this a Spanish or English conversation?") rate of 86.6%. Our analysis highlights what we believe to be interesting new privacy issues in VoIP.

/pdf/language-identification-of-encrypted-voip-traffic-alejandra-32n851prr0.pdf

Language identification of encrypted VoIP traffic: Alejandra y Roberto or Alice and Bob?

A system and method for transmitting high speed data on fixed rate and for variable rate channels. The system and method provides the flexibility of adjusting the data rate, the coding rate, and the nature of individual retransmissions. Further, the system and method supports partial soft combining of retransmitted data with previously transmitted data, supports parity bit selection for successive retransmissions, and supports various combinations of data rate variations, coding rate variations, and partial data transmissions.

Hybrid ARQ schemes with soft combining in variable rate packet data applications

This document specifies a speech codec suitable for robust voice
communication over IP. The codec is developed by Global IP Sound
(GIPS). It is designed for narrow band speech and results in a payload
bit rate of 13.33 kbit/s for 30 ms frames and 15.20 kbit/s for 20 ms
frames. The codec enables graceful speech quality degradation in the
case of lost frames, which occurs in connection with lost or delayed
IP packets. This memo defines an Experimental Protocol for the
Internet community.

Internet Low Bit Rate Codec (iLBC)

In this paper, we discuss the internet low bit rate codec (iLBC) with an emphasis on the frame-independent long-term prediction. The frame-independent long-term prediction is a method to exploit pitch-lag correlations in the encoding of speech without suffering multiple-frame speech degradation in connection with transmission loss. We present mean opinion scores for the iLBC codec and show by means of signal examples how the nature of degradation in a predictive codec based on frame-independent long-term prediction differs from that of traditional CELP codecs.

iLBC - a linear predictive coder with robustness to packet losses

In linear predictive speech coding algorithms, transmission of linear predictive coding (LPC) parameters-often transformed to the line spectrum frequencies (LSF) representation-consumes a large part of the total bit rate of the coder. Typically, the LSF parameters are highly correlated from one frame to the next, and a considerable reduction in bit rate can be achieved by exploiting this interframe correlation. However, interframe coding leads to error propagation if the channel is noisy, which possibly cancels the achievable gain. In this paper, several algorithms for exploiting interframe correlation of LSF parameters are compared. Especially, performance for transmission over noisy channels is examined, and methods to improve noisy channel performance are proposed. By combining an interframe quantizer and a memoryless "safety-net" quantizer, we demonstrate that the advantages of both quantization strategies can be utilized, and the performance for both noiseless and noisy channels improves. The results indicate that the best interframe method performs as good as a memoryless quantizing scheme, with 4 bits less per frame. Subjective listening tests have been employed that verify the results from the objective measurements.

Interframe LSF quantization for noisy channels

In this paper, the performance of different predictive vector quantization (PVQ) structures is studied and compared for different degrees of channel noise. Predictive quantization schemes with an auto-regressive (AR) decoder structure are compared with schemes that employ a moving average (MA) decoder. For noisy channels MA prediction performs better than AR. It is shown here that a combination of a PVQ scheme (AR or MA) and a memoryless VQ outperforms both types of traditional predictive quantizer schemes in noiseless as well as noisy channels.

Predictive VQ for noisy channel spectrum coding: AR or MA?

This paper addresses the problem of efficient transmission of the LSF parameters in speech coding using vector quantization (VQ). By performing a comparison of several memory VQ methods on the same database, we investigate what gains can be achieved by exploiting interframe correlation. The memory VQ methods studied are finite-state VQ and linear predictive VQ. By combining the memory VQ with a fixed memoryless VQ, called the safety-net, further improvements in performance can be obtained. It is found that memory VQ can improve the performance with 3-5 bits compared to memoryless VQ for error-free transmission. The best method in this study is a safety-net extended predictive VQ. For noisy channels, most memory methods perform worse than memoryless VQ, but the safety-net predictive VQ outperforms memoryless VQ for all tested channel error rates, with 4 bits less.

J. Linden

Papers

Internet Low Bit Rate Codec (iLBC)

iLBC - a linear predictive coder with robustness to packet losses

Interframe LSF quantization for noisy channels

Predictive VQ for noisy channel spectrum coding: AR or MA?

Exploiting interframe correlation in spectral quantization: a study of different memory VQ schemes