Packet reordering (RO) is an Internet event that degrades the performance of both TCP and UDP-based applications. In this paper, we present an end-to-end measurement study of packet reordering of UDP traffic. The goal of our measurement study is to characterize packet reordering in the current Internet as it is reflected by PlanetLab infrastructure. Overall, our analysis shows that current UDP traffic reordering is consistent to prior 1990's studies, despite increased Internet load and technology advancements. In addition, our study adds to the previous results by identifying additional reordering characteristics. More specifically, we show that packet reordering is asymmetric as well as temporal and site-dependent, packet size does influence the likelihood of reordering, that there exists a time-of-the-day dependency, and reordering primarily exists at two timescales (a few milliseconds or multiple tens of milliseconds).

We explore Chord-like ring structures for Distributed Hash Tables (DHTs) and show that the Postage Stamp Problem (PSP) is equivalent to finding optimal structures for such ring topologies. We then describe a variant of the PSP that corresponds to ring-like DHTs that use greedy routing and develop an algorithm that smoothly trades off between the number of finger pointers and network diameter. We provide a dynamic programming solution to the number of nodes that can be supported as a function of the number of finger pointers and the network diameter and also note an interesting link to the Fibonacci sequence.

We study the application of unequal loss protection (ULP) algorithms to motion-compensated video over lossy packet networks. In particular, we focus on streaming video applications over the Internet. The original ULP framework applies unequal amounts of forward error correction to embedded data to provide graceful degradation of quality in the presence of increasing packet loss. In this letter, we apply the ULP framework to baseline H.263, a video compression standard that targets low bit rates, by investigating reorderings of the bitstream to make it embedded. The reordering process allows a receiver to display quality video, even at the loss rates encountered in wireless transmissions and the current Internet.

The application of unequal loss protection (ULP) algorithms to motion-compensated video over lossy packet networks is studied. In particular, the focus is on streaming video applications over the Internet. The original ULP framework applies unequal amounts of forward error correction (FEC) to embedded data to provide graceful degradation of quality in the presence of increasing packet loss. The ULP framework is applied to baseline H.263, a non-embedded video compression standard, by investigating re-orderings of the bitstream. The re-ordering process makes the bitstream appear embedded, allowing a receiver to display high quality video even at the high loss rate encountered in wireless transmissions and the current Internet.

We show that the problem of optimal bit allocation among a set of independent discrete quantizers given a budget constraint is equivalent to the multiple choice knapsack problem (MCKP). This result has three implications: first, it provides a trivial proof that the problem of optimal bit allocation is NP-hard and that its related decision problem is NP-complete; second, it unifies research into solving these problems that has to date been done independently in the data compression community and the operations research community; third, many practical algorithms for approximating the optimal solution to MCKP can be used for bit allocation. We implement the GBFOS, partition-search, and Dudzinski-Walukiewicz algorithms and compare their running times for a variety of problem sizes.

We consider the problem of error control for receiver-driven layered multicast of audio and video over the Internet. The sender injects into the network multiple source layers and multiple channel coding (parity) layers, some of which are delayed relative to the source, Each receiver subscribes to the number of source layers and the number of parity layers that optimizes the receiver's quality for its available bandwidth and packet loss probability. We augment this layered FEC system with layered pseudo-ARQ. Although feedback is normally problematic in broadcast situations, ARQ can be simulated by having the receivers subscribe and unsubscribe to the delayed parity layers to receive missing information. This pseudo-ARQ scheme avoids an implosion of repeat requests at the sender and is scalable to an unlimited number of receivers, We show gains of 4-18 dB on channels with 20% loss over systems without error control and additional gains of 1-13 dB when FEC is augmented by pseudo-ARQ in a hybrid system, Optimal error control in the hybrid system is achieved by an optimal policy for a Markov decision process.

We consider the problem of joint source/channel coding of real-time sources, such as audio and video, for the purpose of multicasting over the Internet. The sender injects into the network multiple source layers and multiple channel (parity) layers, some of which are delayed relative to the source. Each receiver subscribes to the number of source layers and the number of channel layers that optimizes the source-channel rate allocation for that receiver's available bandwidth and packet loss probability. We augment this layered FEC system with layered ARQ. Although feedback is normally problematic in broadcast situations, ARQ is simulated by having the receivers subscribe and unsubscribe to the delayed channel coding layers to receive missing information. This pseudo-ARQ scheme avoids an implosion of repeat requests at the sender, and is scalable to an unlimited number of receivers. We show gains of up to 18 dB on channels with 20% loss over systems without error control, and additional gains of up to 13 dB when FEC is augmented by pseudo-ARQ in a hybrid system. The hybrid system is controlled by an optimal policy for a Markov decision process.

This paper describes an algorithm that achieves an approximately optimal assignment of forward error correction to progressive data within the unequal loss protection framework. It first finds the optimal assignment under convex hull and fractional bit allocation assumptions. It then relaxes those constraints to find an assignment that approximates the global optimum. The algorithm has a running time of O(hNlogN) where h is the number of points on the convex hull of the source's utility-cost curve and N is the number of packets transmitted.

We present the unequal loss protection (ULP) framework in which unequal amounts of forward error correction are applied to progressive data to provide graceful degradation of image quality as packet losses increase. We develop a simple algorithm that can find a good assignment within the ULP framework. We use the set partitioning in hierarchical trees coder in this work, but our algorithm can protect any progressive compression scheme. In addition, we promote the use of a PMF of expected channel conditions so that our system can work with almost any model or estimate of packet losses. We find that when optimizing for an exponential packet loss model with a mean loss rate of 20% and using a total rate of 0.2 bits per pixel on the Lenna image, good image quality can be obtained even when 40% of transmitted packets are lost.

We present an algorithm that assigns unequal amounts of forward error correction to progressive data so as to provide graceful degradation as packet losses increase. We use the SPIHT coder to compress images in this work, but our algorithm can protect any progressive compression scheme. The algorithm can also use almost any function as a model of packet loss conditions. We find that for an exponential packet loss model with a mean of 20% and a total rate of 0.2 bpp, good image quality can be obtained, even when 40% of transmitted packets are lost.

We present a simple and efficient scheme for using the Set Partitioning in Hierarchical Trees (SPIHT) image compression algorithm in a generalized multiple description framework. To combat packet loss, controlled amounts of redundancy are added to the original data during the compression process. Unequal loss protection is implemented by varying the amount of redundancy with the importance of data. The algorithm achieves graceful degradation of image quality in the presence of increasing description loss; high image quality is obtained even when over half of the descriptions are lost.

We present an approach to the generalized multiple description problem (V.K. Goyal et al., 1998) that is fundamentally different from previously published algorithms. Our approach uses explicit channel coding in the form of unequal loss protection to obtain a solution that incorporates many important properties: it can be used with any progressive source coder; it generates a balanced encoding with information equally dispersed among the descriptions; it adds a quantifiable amount of redundancy; it adapts that amount of redundancy to expected channel conditions; and it can optimize for different distortion measures. These properties allow the system to gradually improve image quality as the number of received descriptions increases. We compare our system to previously published results and show that forward error correction in multiple description coding can surpass them by a significant margin.

Presents an approach for performing edge detection which builds on prior work in fast facet edge detection using tree-structured vector quantization (TSVQ). The authors first extend the approach by using larger image vectors to reduce computational complexity by performing edge detection on multiple pixels at once. They then reduce the computational complexity of the edge detector without sacrificing performance by pruning the TSVQ with an edge detection-based criterion. They present results of edge detector performance on a sequence of images obtained from a mobile robot.>