In this paper, we shed light on how an adaptive, efficient error coding in the transport layer helps ensure the application's requirements. We recap the use of MDS codes and show that binary coding can significantly reduce the complexity and hence increase the applicability also for embedded devices. We exploit the persymmetric structure of the generator matrix in polar codes to establish a duality of dispersion over channels (the polarization effect) and over packets (the generality required for multicast transmission), thereby constructing systematic polar codes for incremental redundancy whose performance, despite a much lower complexity, is near to MDS codes for medium-range residual loss rates.

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Nowadays Dynamic Adaptive Streaming over HTTP (DASH) is the most prevalent solution on the Internet for multimedia streaming and responsible for the majority of global tra c. DASH uses adaptive bit rate (ABR) algorithms, which select the video quality considering performance metrics such as throughput and playout bu er level. Pensieve is a system that allows to train ABR algorithms using reinforcement learning within a simulated network environment and is outperforming existing approaches in terms of achieved performance. In this paper, we demonstrate that the performance of the trained ABR algorithms depends on the implementation of the simulated environment used to train the neural network. We also show that the used congestion control algorithm impacts the algorithms' performance due to cross-layer e ects.

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The PRRT protocol enables applications with strict performance requirements such as Cyber-Physical Systems, as it provides predictably low, end-to-end delay via cross-layer pacing and timely error correction via Hybrid ARQ (HARQ). However, the implemented HARQ uses computationally complex Maximum Distance Separable (MDS) codes to generate redundancy. In this paper we propose code partitioning for the complexity reduction of MDS codes, thereby enabling their deployment on constrained embedded devices.

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