Exploiting the Path Propagation Time Differences in Multipath Transmission with FEC

We consider a transmission of a delay-sensitive data stream from a single source to a single destination. The reliability of this transmission may suffer from bursty packet losses - the predominant type of failures in today's Internet. An effective and well studied solution to this problem is to protect the data by a Forward Error Correction (FEC) code and send the FEC packets over multiple paths. In this paper we show that the performance of such a multipath FEC scheme can often be further improved. Our key observation is that the propagation times on the available paths often significantly differ, typically by 10-100ms. We propose to exploit these differences by appropriate packet scheduling that we call `Spread'. We evaluate our solution with a precise, analytical formulation and trace-driven simulations. Our studies show that Spread substantially outperforms the state-of-the-art solutions. It typically achieves two- to five-fold improvement (reduction) in the effective loss rate. Or conversely, keeping the same level of effective loss rate, Spread significantly decreases the observed delays and helps fighting the delay jitter.Comment: 12 page

On the correlation of internet packet losses

Copyright © 2008 IEEEIn this paper we analyze more than 100 hours of packet traces from Planet-Lab measurements to study the correlation of Internet packet losses. We first apply statistical tests to identify the correlation timescale of the binary loss data. We find that in half of the traces packet losses are far from independent. More significantly, the correlation timescale of packet losses is correlated with the network load. We then examine the loss runs and the success runs of packets. The loss runs are typically short, regardless of the network load. We find that the success runs in the majority of our traces are also uncorrelated. Furthermore, their correlation timescale also does not depend on the network load. All of these results show that the impact of network load on the correlation of packet losses is nontrivial and that loss runs and success runs are better modeled as being independent than the binary losses themselves. © 2008 IEEE.Hung X. Nguyen and Matthew Rougha

SPAD: a distributed middleware architecture for QoS enhanced alternate path discovery

In the next generation Internet, the network will evolve from a plain communication medium into one that provides endless services to the users. These services will be composed of multiple cooperative distributed application elements. We name these services overlay applications. The cooperative application elements within an overlay application will build a dynamic communication mesh, namely an overlay association. The Quality of Service (QoS) perceived by the users of an overlay application greatly depends on the QoS experienced on the communication paths of the corresponding overlay association. In this paper, we present SPAD (Super-Peer Alternate path Discovery), a distributed middleware architecture that aims at providing enhanced QoS between end-points within an overlay association. To achieve this goal, SPAD provides a complete scheme to discover and utilize composite alternate end-to end paths with better QoS than the path given by the default IP routing mechanisms

Real-Time Neural Video Recovery and Enhancement on Mobile Devices

As mobile devices become increasingly popular for video streaming, it's crucial to optimize the streaming experience for these devices. Although deep learning-based video enhancement techniques are gaining attention, most of them cannot support real-time enhancement on mobile devices. Additionally, many of these techniques are focused solely on super-resolution and cannot handle partial or complete loss or corruption of video frames, which is common on the Internet and wireless networks. To overcome these challenges, we present a novel approach in this paper. Our approach consists of (i) a novel video frame recovery scheme, (ii) a new super-resolution algorithm, and (iii) a receiver enhancement-aware video bit rate adaptation algorithm. We have implemented our approach on an iPhone 12, and it can support 30 frames per second (FPS). We have evaluated our approach in various networks such as WiFi, 3G, 4G, and 5G networks. Our evaluation shows that our approach enables real-time enhancement and results in a significant increase in video QoE (Quality of Experience) of 24\% - 82\% in our video streaming system