Systems for assisted and autonomous driving increasingly depend on information received and updated through wireless communication. But wireless communication often faces performance degradation because of its dynamic...
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ISBN:
(纸本)9781538676462
Systems for assisted and autonomous driving increasingly depend on information received and updated through wireless communication. But wireless communication often faces performance degradation because of its dynamic nature. Using multiple available communication channels, such as WiFi, LTE or 5G New Radio, simultaneously can increase the throughput and reliability, but also increases the dynamics of the system. MPTCP estimates the channel capacity and latency and schedule packets accordingly. However, in conditions with unstable channels MPTCP fails to fully utilize the available capacity. In this paper, we propose the use of Network coding to efficiently utilize the available resources. We use a channel agnostic, random scheduler to maximize the utilization of all available channels. This prevents underestimations, but also produces a high number of packet loss and duplicate transmissions. We use Network coding to repair the losses and reduce the overhead of redundant data. Our implementation of this protocol is evaluated against MPTCP in an emulated multipath network with time-varying path properties. The evaluation shows, that the proposed protocol utilizes the channels efficiently even in unstable conditions. In the evaluated dynamic network, the proposed protocol efficiently utilizes 94% of the available capacity, while MPTCP is below 80% due to underestimation. While our protocol is not suitable for general purpose traffic, it provides good performance for large file transfers in unstable wireless multipath networks.
Network coding can ease the block scheduling and thus makes the distribution more efficient. However, the complexities of encoding and decoding increase sharply as the content size scales up. In this paper, we propose...
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ISBN:
(纸本)9781424450053
Network coding can ease the block scheduling and thus makes the distribution more efficient. However, the complexities of encoding and decoding increase sharply as the content size scales up. In this paper, we propose a coding scheme which combines chunked coding and sparse linear network coding to reduce both encoding and decoding costs of content distribution. Based on the combined scheme, we implement a P2P content distribution system, named Dasher, where Local-Rarest-First scheme is adopted for chunk scheduling. Under the same system architecture, we implement three comparative systems, a BitTorrent-like system, named Mybt, a system only with sparse coding, named Sparser and a system using chunked coding, named Chunker. We conduct extensive experiments to compare the performance among these four systems. The experimental results show that Dasher with certain chunk sizes can reduce the average downloading time up to 15% compared with Mybt, and up to 43% with Chunker. With proper chunk sizes, the downloading time of Dasher is almost the same with Sparser. The average decoding rate of Dasher is the same with Chunker, and is nearly m times as fast as Sparser, where m is the number of chunks. Moreover, with respect to robustness, Dasher performs almost as well as Chunker, better than Mybt, but worse than Sparser.
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