Impact Of Packet Loss Research Articles

A no-reference low-complexity QoE measurement algorithm for H.264 video transmission systems

Quality of Experience (QoE) measurement for transmitted video sequences over packet error prone channels is an inevitable necessity. Due to the intrinsic properties of packet error prone channels in imposing quality degradation on transmitted media and also, the costly and time-consuming nature of subjective quality measurement techniques, exact modeling of the impact of packet loss on measured video quality based on on-line objective measurement methods is an important task. In the current work, a low-complexity objective video quality measurement algorithm is developed by which by considering various factors that may affect video quality, we can estimate subjective video quality with acceptable accuracy. Then, the performance of the proposed objective algorithm is compared with popular objective SSIM/VQM techniques. The simulation results verify that the proposed algorithm has high level of accuracy. On the other hand, the proposed algorithm has low-complexity which makes it suitable for online implementation. Some important and related patents in the field of QoE measurements and monitoring in networks are investigated.

Enhancing Sensor Network Data Quality via Collaborated Circuit and Network Operations

In many applications, the quality of data gathered by sensor networks is directly related to the signal-to-noise ratio (SNR) of the sensor data being transmitted in the networks. Different from the SNR that is often used in measuring the quality of communication links, the SNR used in this work measures how accurately the data in the network packets represent the physical parameters being sensed. Hence, the signal here refers to the physical parameters that are being monitored by sensor networks; the noise is due to environmental interference and circuit noises at sensor nodes, and packet loss during network transmission. While issues affecting SNR at sensor nodes have been intensively investigated, the impact of network packet loss on data SNR has not attracted significant attention in sensor network design. This paper investigates the impact of packet loss on sensor network data SNR and shows that data SNR is dramatically affected by network packet loss. A data quality metric, based on data SNR, is developed and a cross-layer adaptive scheme is presented to minimize data quality degradation in congested sensor networks. The proposed scheme consists of adaptive downsampling and bit truncation at sensor nodes and intelligent traffic management techniques at the network level. Simulation results are presented to demonstrate the validity and effectiveness of the proposed techniques.

QoE enhancement for video transmission over MANETs using distortion minimization

Quality of Experience (QoE) enhancement for a transmitted video sequence in Mobile Ad hoc NETworks (MANETs) is a challenging and important issue in the networking research community. Intrinsic high levels of packet error probability in MANETs can cause high levels of distortion based on the position of packet loss in transmitted frames. Hence, exact modeling of the impact of packet loss on video quality and the resulting distortion is an important task. Many conventional distortion modeling techniques simply consider a linear relationship between the packet loss and distortion, which is inaccurate. The main contributions of the current work are twofold. At first, an accurate model has been developed, which can capture the exact effect of network packet loss on video quality performance (and hence on the QoE) with Group Of Picture (GOP)-level granularity. Then, based on this model, an optimal bandwidth allocation strategy is developed in MANETs, with which, based on some network-specific constraints, the loss-induced distortion associated with a video source is minimized, using some cross-layer design techniques. Finally, the resulting optimal rates can be used as rate-feedbacks for on-line rate adaptation of an appropriate video encoder, such as H.264/AVC. Numerical analysis verifies the theoretical results.

Predicting the Quality of Synthesized and Natural Speech Impaired by Packet Loss and Coding Using PESQ and P.563 Models

This paper investigates the impact of independent and dependent losses and coding on speech quality predictions provided by PESQ (also known as ITU-T P.862) and P.563 models, when both naturally-produced and synthesized speech are used. Two synthesized speech samples generated with two different Text-to-Speech systems and one naturally-produced sample are investigated. In addition, we assess the variability of PESQ's and P.563's predictions with respect to the type of speech used (naturally-produced or synthesized) and loss conditions as well as their accuracy, by comparing the predictions with subjective assessments. The results show that there is no difference between the impact of packet loss on naturally-produced speech and synthesized speech. On the other hand, the impact of coding is different for the two types of stimuli. In addition, synthesized speech seems to be insensitive to degradations provided by most of the codecs investigated here. The reasons for those findings are particularly discussed. Finally, it is concluded that both models are capable of predicting the quality of transmitted synthesized speech under the investigated conditions to a certain degree. As expected, PESQ achieves the best performance over almost all of the investigated conditions.

Video transmission over MANETs with enhanced quality of experience

Quality of Experience (QoE) enhancement for a transmitted video sequence in Mobile Ad hoc NETworks (MANETs) is a challenging and important issue in networking research community. Intrinsic high levels of packet error probability in MANETs can cause high levels of distortion based on the position of packet loss in transmitted Intra-coded (I), Predicted (P) and Bi-predictive (B) frames. Hence, exact modeling of the impact of packet loss on the video quality and the resulting distortion is an important task. Many conventional distortion modeling techniques simply consider a linear relationship between the packet loss and distortion which is inaccurate. The main contributions of the current work are twofold. At first, an accurate model has been developed which can capture the exact effect of network packet loss on the video quality performance (and hence on the QoE) with Group Of Picture (GOP)-level granularity. Then, based on this model, an optimal bandwidth allocation strategy is being developed in MANETs which based on some network-specific constraints; the loss-induced distortion associated with a video source is being minimized using some cross-layer design techniques. Finally, the resulting optimal rates can be used as rate-feedbacks for on-line rate adaptation of an appropriate video encoder such as H.264/AVC. Numerical analysis has been presented to validate the theoretical results and to verify the claims.

Energy-efficient cooperative communication for data transmission in wireless sensor networks

It is a practical challenge to provide reliable and efficient communication for data transmission in wireless sensor networks. To recover from packet losses, conventional approaches tried to use retransmission or FEC mechanisms. However, these mechanisms may introduce excessive energy overhead for reliability guarantee. By exploiting the wireless broadcast nature and the node overhearing capability, we propose a novel cooperative communication scheme EECC to improve data transmission performance for wireless sensor networks. In this scheme, cooperative reply is performed at each hop by the best-suited node elected from those that have successfully overheard the transmitted packet. EECC is not a routing protocol but rather works as an augment to minimize the impact of packet losses on network performance. Extensive analytical and experimental results confirm that our scheme is very effective in improving both energy efficiency and end-to-end delay for data transmission in lossy networks.

Mobile TV services through IP Datacast over DVB-H: Dependability of the quality of experience on the IP-based distribution network quality of service

The Digital Video Broadcast-Handheld (DVB-H) is an international standard which offers broadcast data services to mobile handheld devices. Its main enhancements to conventional Digital Video Broadcast-Terrestrial (DVB-T) systems include the addition of time-slicing and an extra stage of error correction, referred to as Multi-Protocol Encapsulation-Forward Error Correction (MPE-FEC) at the link layer. The essential components required to deploy an IP-based mobile TV service on top of DVB-H are described by the DVB standard for IP DataCast specifications (DVB-IPDC). Since mobile TV services mostly consist of real-time video data, very stringent demands in terms of quality of service (QoS) are imposed to the broadcast operator's primary distribution network. In this article, the impact of packet loss, introduced in a IP-based distribution network, on the quality of experience (QoE), the video quality as perceived by the end user, is characterised. In recent work, a first analysis of the impact of random packet loss was reported upon. This paper describes a more profound analysis of both random and burst packet loss by taking into account several objective video quality metrics. It is shown that, although MPE-FEC is introduced for better reception between antenna and receiver, its use has a major impact on the effects of packet loss in the distribution network. The use of error-correction techniques in the distribution network only has a beneficial impact on the QoE when lower MPE-FEC code rates are used. Moreover, the impact of action scenes or scene switches on the perceived video quality is also characterised in detail. Based on the experimental results, an analytical model is presented which allows to predict the QoE detoriation as a function of the burst loss probability in the distribution network.

The Tool for Quality Estimation of Short Voice Segments

It is recognized that objective and reliable voice quality measure is PESQ algorithm. Systemic analysis of impact of packet loss on voice quality using PESQ raises some problems because each and every packet has distinct impact on speech quality and long signal as required in ITU specification [2] contains a lot of positions for possible packet losses. When there are a few lost packets in single measurement, the quality measured become a random number. The generalized conclusions about impact of packet loss on voice quality may be educed after many measurements performed using different combinations of lost packets. In this paper proposed tool for quality measurement of short segments using composite measurement signal with conventional PESQ algorithm. It is shown that the proposed tool is compatible with existing PESQ algorithm. The measurement results obtained using composite signals are compatible with those generated by standard PESQ. The proposed tool with composite measurement signal reduces the amount of experiments because the number of voice frames is smaller in short sentences.

Random linear network coding with ladder-shaped global coding matrix for robust video transmission

Robust video multicast in erasure networks using network coding (NC) to reduce the impact of packet loss is studied in this paper. In our proposed solution, random linear network coding (RLNC) is adopted at intermediate nodes of the network. RLNC linearly combines a group of packets by randomly selecting weighting coefficients on a finite field, and the loss of an RLNC-coded packet is equivalent to the loss of one constraint in a linear system of equations required for RLNC decoding. Unless the global coding coefficient matrix, or simply called the global coding matrix (GCM), is of full rank, a receive node cannot reconstruct all source packets. To address this rank deficiency problem, we propose to construct a special-structured GCM, called the ladder-shaped GCM (LGCM), for layered H.264/SVC (scalable video coding) video multicast. The ladder shape of the sparse coding matrix is maintained throughout the RLNC process to achieve two objectives: (1) to enable partial decoding of a block; and (2) to provide unequal erasure protection for H.264/SVC priority layers. Furthermore, quality degradation is minimized by optimizing the amount of redundancy assigned to each layer, and graceful quality degradation is achieved by error concealment (EC). Simulation results are given to demonstrate the superior performance of the proposed RLNC–LGCM scheme over the traditional RLNC with a generalGCM.

Study For Congestion Control in Mobile Adhoc Networks

In recent years a number of papers have presented theoretical solutions to this problem that are based on combining differential-backlog scheduling algorithms with Utility-based congestion control .Reducing packet loss in MANETs typically involves congestion control running on top of a mobility and failure adaptive routing protocol at the network layer. In the current designs, routing is not congestion-adaptive. Routing may let a congestion happen, which is detected by congestion control, but, to deal with this fact, it may be too late (i.e., long delay and many packets already lost) or require significant overhead if a new route is needed. This problem becomes more visible especially in large-scale transmission of high traffic such as multimedia data, where congestion is more probable and the negative impact of packet loss on the service quality is more of significance.

Viewing impaired video transmissions from a modeling perspective

The provisioning of broadband access for mass market enables multimedia content to be extensively viewed and exchanged over the Internet. Consequently, Quality of Experience (QoE) aspects, describing the service quality perceived by the user, become vital factors in ensuring customer satisfaction in today’s networks. In the case of streaming video, enforcing QoE is a complex research topic [4] involving various aspects such as the characteristics of network traffic, codec functions, and measures of user perception of video quality. For instance, the impact of packet loss on the user’s perception of real-time video can be investigated by using measured packet loss traces or generated by analytical models. These traces are further used to impair video sequences at different packet loss rates, whereas visual quality properties are then derived by decoding the produced sequences. Both measurement and model based generators for loss traces have advantages and disadvantages. A loss trace obtained from measurements accurately reflects the state of the network. On the other hand, fitting statistical properties observed in measurements to predefined loss models allows producing multiple loss traces with various properties, such as packet loss rates and loss burst length. This flexibility permits studying the behavior of video sequences impaired under different error conditions. A disadvantage of model generated loss traces is that the statistical properties may not adequately replicate those of the measured trace, as they are likely to be biased by model limitations. Consequently, analytical models are needed to closely replicate the loss patterns observed in measurements. Finite-state Markov chains are a particular class of models for generating packet loss traces. In general, these models are widely used to characterize error processes in telecommunication systems and to evaluate the performance of coding or other measures for error resilience [5, 6, 9]. A large body of work focuses on replicating loss burst and gap length distributions which are relevant for evaluating the performance of FEC algorithms. In the case of encoded video, however, errors over larger time-scales than burst and gaps may cause various visual effects, e.g., distorted areas of an image. Depending on the bitrate, time-scales in video can range over multiple orders of magnitude from microseconds in the case of the transmission of a single macroblock—a small part of a still image—up to seconds for the transmission of an entire group of pictures. Moreover, with the advent of the latest video encoding standard H.264 [3], parameter sets describing a set of pictures with similar decoding properties can introduce even larger time-scales. While losing a single macroblock can have a minor impact by affecting only a small fraction of the image, losing a parameter set can have a severe effect on the decoding process. This consideration suggests to focus on the loss process over multiple time-scales rather than on matching burst and gap length distributions. To address this observation, this paper uses an M -state Markov chain to generate loss traces for impairing video transmissions. To this end, Section 2 provides a general result for M -state Markov chains to describe the distribution of the packet losses over multiple time-scales using secondorder statistics. By using moment matching, 2-state Markov models are fitted to a wireless Digital Video Broadcasting (DVB-H) loss trace. Then Section 3 presents numerical results which illustrate that the visual impairment patterns depend on the chosen modeling technique. For future work we plan to investigate relevant time-scales for the fitting process. Moreover, we plan to evaluate whether increasing the number of states of the underlying Markov chain results in any significant gain. This paper is an extension of [1] where we mainly focused on fitting 2-state Markov models to DVB-H and backbone packet loss traces using secondorder statistics.

On the Capacity ofAd HocNetworks Under Random Packet Losses

We consider the problem of determining asymptotic bounds on the capacity of a random <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hoc</i> network. Previous approaches assumed a link layer model in which if a transmitter-receiver pair can communicate with each other, i.e., the signal to interference and noise ratio (SINR) is above a certain threshold, then the transmitted packet is received error-free by the receiver thereby. Using this model, the per node capacity of the network was shown to be Theta( <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">radic(n</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">log</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n)</sub> / <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ). In reality, for any finite link SINR, there is a nonzero probability of erroneous reception of the packet. We show that in a large network, as the packet travels an asymptotically large number of hops from source to destination, the cumulative impact of packet losses over intermediate links results in a per-node throughput of only <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">radic(n)</sub> / <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ) under the previously proposed routing and scheduling strategy. We then propose a new scheduling scheme to counter this effect. The proposed scheme provides tight guarantees on end-to-end packet loss probability, and improves the per-node throughput to Omega( <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">radic(n)(log</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n)</sub> / <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> 2 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(alpha-2)</sub> / <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alpha+2</sup> ) where <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alpha</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</i> is the path loss exponent.

Peculiarities of Testing the Impact of Packet Loss on Voice Quality

Some packets may be lost or corrupted when voice is transmitted using radio channel. Current voice coders use complex techniques for masking of lost segments. Thus overall speech quality is improved. This work shows experimentally that masking technique changes not only signal corresponding to lost frame, but also subsequent signal. Work investigates properties of PESQ algorithm when quality degradation is due to localized frame errors. The impact of measurement window length and position is investigated using theoretical and experimental methods. It is shown that longer measurement window artificially decreases the impact of localized frame error. Also it is shown that position of measurement window influences measured result. These experiments show high uncertainty may result from using standardized PESQ algorithm for the signals with localized errors. We propose a method for reduction of measurement uncertainty witch is based on artificially constructed voice signal and standard PESQ algorithm. Ill. 11, bibl. 10 (in English; summaries in English, Russian and Lithuanian).

Modeling Best-Effort and FEC Streaming of Scalable Video in Lossy Network Channels

Video applications that transport delay-sensitive multimedia over best-effort networks usually require special mechanisms that can overcome packet loss without using retransmission. In response to this demand, forward-error correction (FEC) is often used in streaming applications to protect video and audio data in lossy network paths; however, studies in the literature report conflicting results on the benefits of FEC over best-effort streaming. To address this uncertainty, we start with a baseline case that examines the impact of packet loss on scalable (FGS-like) video in best-effort networks and derive a closed-form expression for the loss penalty imposed on embedded coding schemes under several simple loss models. Through this analysis, we find that the utility (i.e., usefulness to the user) of unprotected video converges to zero as streaming rates become high. We then study FEC-protected video streaming, re-derive the same utility metric, and show that for all values of loss rate inclusion of FEC overhead substantially improves the utility of video compared to the best-effort case. We finish the paper by constructing a dynamic controller on the amount of FEC that maximizes the utility of scalable video and show that the resulting system achieves a significantly better PSNR quality than alternative fixed-overhead methods

Congestion Adaptive Routing in Mobile Ad Hoc Networks

Mobility, channel error, and congestion are the main causes for packet loss in mobile ad hoc networks. Reducing packet loss typically involves congestion control operating on top of a mobility and failure adaptive routing protocol at the network layer. In the current designs, routing is not congestion-adaptive. Routing may let a congestion happen which is detected by congestion control, but dealing with congestion in this reactive manner results in longer delay and unnecessary packet loss and requires significant overhead if a new route is needed. This problem becomes more visible especially in large-scale transmission of heavy traffic such as multimedia data, where congestion is more probable and the negative impact of packet loss on the service quality is of more significance. We argue that routing should not only be aware of, but also be adaptive to, network congestion. Hence, we propose a routing protocol (CRP) with such properties. Our ns-2 simulation results confirm that CRP improves the packet loss rate and end-to-end delay while enjoying significantly smaller protocol overhead and higher energy efficiency as compared to AODV and DSR

Quality Relief of Broadcast-quality Video with Net-work Packet Losses

The impact of packet losses on broadcast-quality video transmission through an asynchronous network is discussed based on the quality of the video playback. The main requirements for broadcast-quality video transmission are quality, synchronization, and smooth failover. Therefore, video quality is also related to network quality in an asynchronous network. When video frames that contain network packet losses are decoded, noise-free video cannot be maintained for block noises caused in video playback. One of the most important features of real-time video data transmission through an asynchronous network is the ability to correct and conceal errors. Adding redundant bits to the source information, as in forward error correction and error correcting code, increases the data bandwidth. Adding redundancy, such as by interleaving, lengthens the latency of the video playback. The appropriate redundancy factor, therefore should ensure a real-time response, minimize latency, and maintain video playback quality. Therefore, when selecting an appropriate error correction/concealment code, it is necessary to clarify the impact of successive packet losses on the quality degradation of broadcast-quality video. To clarify the relationship between the broadcast-quality video quality and network packet losses, we analyzed data based on observation of an actual network with the loss window size as a parameter. Next, we discussed the characteristics of the block noises on the video playback using the Markov model. For this purpose, we made an 80, 000-km-long ATM path on the Japan Gigabit Network and examined the peak-signal to noise ratio of the SMPTE-314M.video playback that contained ATM cell losses.

Quality Monitoring of Video Over a Packet Network

We consider monitoring the quality of compressed video transmitted over a packet network from the perspective of a network service provider. Our focus is on no-reference methods, which do not access the original signal, and on evaluating the impact of packet losses on quality. We present three methods to estimate mean squared error (MSE) due to packet losses directly from the video bitstream. NoParse uses only network-level measurements (like packet loss rate), QuickParse extracts the spatio-temporal extent of the impact of the loss, and FullParse extracts sequence-specific information including spatio-temporal activity and the effects of error propagation. Our simulation results with MPEG-2 video subjected to transport packet losses illustrate the performance possible using the three methods.

Simple analysis of the impact of packet loss and delay on voice transmission quality

In this paper a simple analytical expression based on E-model is applied to analyse the impact of packet loss and delay on voice transmission quality. The relationship between overall transmission quality rating, packet loss and delay G.711, G.723 and G.729 codecs is presented.

Spectral analysis of packet loss rate at a statistical multiplexer for multimedia services

Uses the well known technique of power spectral representation to characterize the second order statistics of packet loss during congestion at a statistical multiplexer. Unlike the steady state statistics, the second order statistics measures the time correlation behavior of loss. Typically, the low frequency loss power is associated with long periods of highly consecutive loss. The effect of the loss rate power spectrum on transmission qualities is service dependent. The study indicates that, by selective packet discarding, one can properly change the distribution of the overall loss rate spectrum among the multiplexed traffic streams, which can significantly improve the transmission quality of individual services. Moreover, with the design of multi-level buffer overload control, one can tune the loss rate steady state distribution of different priority streams to a piece-wise step function, which minimizes the packet loss impact on service qualities.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>