Bandwidth adaptation for real-time streaming applications is typically designed to be conservative, since pushing for higher bandwidth could be counterproductive if it means an increased latency. However, such bandwidth adaptation operates based on the "symptoms" of congestion (e.g., increased delay) without knowing the underlying cause (self-congestion vs. cross-traffic). In this paper, we consider this problem in the context of Wi-Fi networks and introduce a novel technique, Ping-Pair, to measure and attribute congestion. We have integrated Ping-Pair into the popular Skype audio-video conferencing application to enable improved bandwidth adaptation dubbed Kwikr, using which we have conducted controlled experiments and also randomized A/B tests in a production setting.

In this paper, we introduce an app-radio cross-layer framework for improving Quality of user Experience (QoE) of Over-The-Top (OTT) Internet applications in Mobile Broadband Networks, e.g. WiMAX, 3G, LTE, etc. We apply methods similar to the well-known network layer techniques: Explicit Congestion Notification (ECN) and Differentiated Services (DiffServ) to application layer and wireless link layer. We focus on UDP based delay-sensitive real-time video call applications such as Skype, and propose to exchange cross-layer information, e.g. congestion, packet priority, etc., through API (application programming interface) based control signals instead of the existing ECN and DiffServ fields in IP data packet header. We built a prototype system with Microsoft Windows XP OS and Intel Mobile WiMAX network adapter, and use POLQA Mean Opinion Score (MOS) to measure the audio QoE for the video call. Empirical results show that immediate congestion indication effectively speeds up Skype response to bandwidth variation, and intra-flow prioritization further reduces the delay of audio packets. Both significantly improve the audio MOS of a video call.

Packet loss concealment (PLC) at a receiver has a substantial effect on the speech quality in voice over IP. Most conventional PLC systems have largely relied upon variations of signal repetition and overlap-add interpolation which can produce speech signals that do not follow the larger overall statistical trends. in this paper, we demonstrate how hidden Markov models can be utilized to effect PLC based on statistical signal processing. In particular, we show how HMM-based PLC yields conditional density functions that can be utilized by various statistical estimation methods that produce signal parameter estimates that produce more natural variation than conventional PLC methods, thereby providing much better speech quality

In this paper, novel techniques for packet loss robust speech coding are proposed. By exploiting knowledge of the receiving end packet loss concealment algorithm, an existing rate-distortion optimal time-segmentation algorithm is extended to taking packet losses into account. To increase robustness in highly nonstationary signals, the technique is complemented by a redundancy selection scheme. A jointly optimal approach ensures that the complementarity between time-segmentation and redundancies is fully exploited. The performance of the methods is investigated through Monte Carlo simulations under various conditions, such as rate, packet loss probability, and algorithmic delay. Finally, subjective listening tests demonstrate perceptual improvements as compared to conventional adaptive time-segmentation not taking packet losses into account.

As voice over IP proliferates, packet loss concealment (PLC) at the receiver has emerged as an important factor in determining voice quality of service. Through the use of heuristic variations of signal and parameter repetition and overlap-add interpolation to handle packet loss, conventional PLC systems largely ignore the dynamics of the statistical evolution of the speech signal, possibly leading to perceptually annoying artifacts. To address this problem, we propose the use of hidden Markov models for PLC. With a hidden Markov model (HMM) tracking the evolution of speech signal parameters, we demonstrate how PLC is performed within a statistical signal processing framework. Moreover, we show how the HMM is used to index a specially designed PLC module for the particular signal context, leading to signal-contingent PLC. Simulation examples, objective tests, and subjective listening tests are provided showing the ability of an HMM-based PLC built with a sinusoidal analysis/synthesis model to provide better loss concealment than a conventional PLC based on the same sinusoidal model for all types of speech signals, including onsets and signal transitions

We investigate the trade-off between delay and signal quality in adaptive time-segmentation for speech coding. A variable rate sinusoidal coder with adaptive segmentation and bit allocations is proposed and implemented with specifiable look-ahead. Objective and subjective results indicate that adaptive time-segmentation is advantageous even with low delay (30 ms), and that quality only increases with the delay until approximately 100 ms.

We consider the problem of packet loss concealment for voice over IP (VoIP). The speech signal is compressed at the transmitter using a sinusoidal coding scheme working at 8 kbit/s. At the receiver, packet loss concealment is carried out working directly on the quantized sinusoidal parameters, based on time-scaling of the packets surrounding the missing ones. Subjective listening tests show promising results indicating the potential of sinusoidal speech coding for VoIP.

A novel signal subspace method for speech enhancement is proposed. The algorithm is derived from the filterbank interpretation of the truncated (quotient) singular value decomposition (T(Q)SVD) algorithm. We derive a recursive version of this algorithm which results in a recursively updated eigenfilterbank. The proposed method benefits from a low system delay and a low amount of musical noise in the enhanced speech signal.