Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 0706.1395

Opportunistic Network Coding for Video Streaming over Wireless

Hulya Seferoglu·Athina Markopoulou
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this paper, we study video streaming over wireless networks with network coding capabilities. We build upon recent work, which demonstrated that network coding can increase throughput over a broadcast medium, by mixing packets from different flows into a single packet, thus increasing the information content per transmission. Our key insight is that, when the transmitted flows are video streams, network codes should be selected so as to maximize not only the network throughput but also the video quality. We propose video-aware opportunistic network coding schemes that take into account both (i) the decodability of network codes by several receivers and (ii) the importance and deadlines of video packets. Simulation results show that our schemes significantly improve both video quality and throughput.

Explore related subjects

cs.NI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hulya Seferoglu, Athina Markopoulou. 2007-06-11. Opportunistic Network Coding for Video Streaming over Wireless. https://arxiv.org/abs/0706.1395

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

On the Design of Physical Topology in OCS-based Clusters

Optical Circuit Switches (OCSes) are increasingly deployed in data center networks and AI clusters. By wiring the electrical ports of GPUs and electrical switches to the OCS ports, the physical interconnection pattern defines a \emph{physical topology}. \textbf{Topology Engineering (ToE)} seeks an OCS configuration that optimizes a traffic objective given traffic demands and physical constraints, but solving this joint problem is computationally expensive. A common approach is to \emph{decouple} the problem into two steps: first, compute a \emph{logical topology} that satisfies the traffic demands without considering the physical topology constraints and then map it onto the physical topology to obtain the OCS configuration. This, however, introduces a quality loss issue: the resulting logical topology sometimes cannot be realized exactly under the given physical topology. To address this problem, we show that it stems from a property missing in the physical topology. We propose \textbf{Decoupling Optimality} and the necessary and sufficient conditions under which a decoupled method will not lose quality. Examining existing physical topologies against these conditions reveals that they either fail them, or satisfy them at the cost of cluster scale or deployment generality. Our proposed \textbf{Cross Wiring} meets all the conditions, without sacrificing cluster scale or adding devices. On a 128-NPU testbed, the lack of \emph{Decoupling Optimality} increases training iteration time by up to 39.5\%, and a microbenchmark validates the importance of \emph{Decoupling Optimality} in both solution quality and solving speed. A testbed built from two MEMS OCSes validates the engineering feasibility of Cross Wiring.

cs.NI↗
arXiv2026

Can LLMs help find Ambiguities in Protocol Specifications?

Internet protocol specifications written in RFCs are subject to ambiguities and multiple interpretations that can cause interoperability failure. While these have presumably cleared up after years of experience, such ambiguities can bedevil the adoption of newer protocols like 5G. The 5G specifications pair a formal message syntax (ASN.1) with message-handling procedures written in natural language. This creates semantic underspecification: a syntactically valid message can reach a state whose procedures never say how to handle it, so standard-compliant implementations diverge. We frame this as a gap or a fork in a partially specified communicating state machine, and present SpecLens, which puts that view in front of a language model as a scaffold. Stronger models do not remove the need for it: they broaden the search without disciplining it, and fewer than half their findings survive inspection. Across 36 procedures from six 3GPP and O-RAN protocols, experts accept 185 of 197 SpecLens findings, and 60 drive observable divergence between the OpenAirInterface and srsRAN implementations under differential test. While we use 5G as a canonical example of a newer protocol, we also show ambiguity results for the more mature DNS protocol.

cs.NI↗
arXiv2026

MoSE: Mode-Switching Expander for Mixed LLM Training and Inference

AI clusters increasingly run large language model (LLM) inference and training on the same fabric. Prefill-decode (P-D) disaggregation creates key-value (KV) cache transfers between prefill and decode groups, whereas training collectives and all-to-all traffic benefit from near-uniform global connectivity. A static sparse topology can therefore be poorly matched to one of the two traffic patterns. We present Mode-Switching Expander (MoSE), a reconfigurable expander that treats topology design as a fixed-degree edge-allocation problem. MoSE reallocates the same sparse edge budget toward direct P-D connectivity in inference-heavy modes and restores a uniform random regular expander in training-heavy modes. We evaluate MoSE using a 1024-group flow-level topology model, shortest-path routing, and two mixed workloads. Across 20 seeds, MoSE reduces average and 95th-percentile (P95) load-aware KV communication cost by 90.8\% and 91.9\% relative to Static-Training in the inference-heavy mode. In the training-heavy mode, it reduces average and P95 training communication cost by 22.7\% and 27.6\% relative to stale Static-Inference. These results show that coarse-grained topology switching can support both workload modes without additional ports or routing changes.

cs.NI↗