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Recent work on peer-to-peer systems has demonstrated the ability to deliver low latencies and good load balance when demand for data items is relatively uniform. We describe a lightweight, adaptive, and system-neutral replication protocol, LAR, that delivers low latencies and good load balance even when demand is heavily skewed.Simulation of LAR in combination with both the Chord and TerraDir systems shows that LAR quickly adapts to non-uniformity in both the underlying system topology and in the input stream. Further, we demonstrate better performance than functionally similar application-layer protocols, using an order of magnitude less network bandwidth.

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Existing video streaming algorithms use various estimation approaches to infer the inherently variable bandwidth in cellular networks, which often leads to reduced quality of experience (QoE). We ask the question: "If accurate bandwidth prediction were possible in a cellular network, how much can we improve video QoE?". Assuming we know the bandwidth for the entire video session, we show that existing streaming algorithms only achieve between 69%-86% of optimal quality. Since such knowledge may be impractical, we study algorithms that know the available bandwidth for a few seconds into the future. We observe that prediction alone is not sufficient and can in fact lead to degraded QoE. However, when combined with rate stabilization functions, prediction outperforms existing algorithms and reduces the gap with optimal to 4%. Our results lead us to believe that cellular operators and content providers can tremendously improve video QoE by predicting available bandwidth and sharing it through APIs.