This paper proposes a video delivery strategy for dynamic streaming services which maximizes time-average streaming quality under a playback delay constraint in wireless caching networks. The network where popular videos encoded by scalable video coding are already stored in randomly distributed caching nodes is considered under adaptive video streaming concepts, and distance-based interference management is investigated in this paper. In this network model, a streaming user makes delay-constrained decisions depending on stochastic network states: 1) caching node for video delivery, 2) video quality, and 3) the quantity of video chunks to receive. Since wireless link activation for video delivery may introduce delays, different timescales for updating caching node association, video quality adaptation, and chunk amounts are considered. After associating with a caching node for video delivery, the streaming user chooses combinations of quality and chunk amounts in the small timescale. The dynamic decision making process for video quality and chunk amounts at each slot is modeled using Markov decision process, and the caching node decision is made based on the framework of Lyapunov optimization. Our intensive simulations verify that the proposed video delivery algorithm works reliably and also can control the tradeoff between video quality and playback latency.

Motivation: In DNA storage systems, there are tradeoffs between writing and reading costs. Increasing the code rate of error-correcting codes may save writing cost, but it will need more sequence reads for data retrieval. There is potentially a way to improve sequencing and decoding processes in such a way that the reading cost induced by this tradeoff is reduced without increasing the writing cost. In past researches, clustering, alignment and decoding processes were considered as separate stages but we believe that using the information from all these processes together may improve decoding performance. Actual experiments of DNA synthesis and sequencing should be performed because simulations cannot be relied on to cover all error possibilities in practical circumstances. Results: For DNA storage systems using fountain code and Reed-Solomon (RS) code, we introduce several techniques to improve the decoding performance. We designed the decoding process focusing on the cooperation of key components: Hamming-distance based clustering, discarding of abnormal sequence reads, RS error correction as well as detection and quality score-based ordering of sequences. We synthesized 513.6 KB data into DNA oligo pools and sequenced this data successfully with Illumina MiSeq instrument. Compared to Erlich’s research, the proposed decoding method additionally incorporates sequence reads with minor errors which had been discarded before, and thus was able to make use of 10.6–11.9% more sequence reads from the same sequencing environment, this resulted in 6.5–8.9% reduction in the reading cost. Channel characteristics including sequence coverage and read-length distributions are provided as well. Availability and implementation: The raw data files and the source codes of our experiments are available at: https://github.com/jhjeong0702/dna-storage.

We begin by presenting a simple lossy compressor operating at near-zero rate: The encoder merely describes the indices of the few maximal source components, while the decoder's reconstruction is a natural estimate of the source components based on this information. This scheme turns out to be near optimal for the memoryless Gaussian source in the sense of achieving the zero-rate slope of its distortion-rate function. Motivated by this finding, we then propose a scheme comprised of iterating the above lossy compressor on an appropriately transformed version of the difference between the source and its reconstruction from the previous iteration. The proposed scheme achieves the rate distortion function of the Gaussian memoryless source (under squared error distortion) when employed on any finite-variance ergodic source. It further possesses desirable properties, and we, respectively, refer to as infinitesimal successive refinability, ratelessness, and complete separability. Its storage and computation requirements are of order no more than (n ² )/(log ^β n) per source symbol for β > 0 at both the encoder and the decoder. Though the details of its derivation, construction, and analysis differ considerably, we discuss similarities between the proposed scheme and the recently introduced Sparse Regression Codes of Venkataramanan et al.

Due to the advantages of high storage densities and longevity, DNA storage has become one of the attractive technologies for future data storage systems. However, the writing/reading cost is still high and more efficient techniques for DNA storage are required. In this paper, we propose improved log-likelihood ratio (LLR) processing schemes based on observed statistics for low-density parity-check (LDPC) code decoding to reduce reading cost while encoding schemes are kept unchanged. Due to the mismatch between the real channel and the observed statistics and also the limit of maximum decoder input value, scaling the magnitude of LLR can lead to a better error correcting performance. Therefore, we propose two strategies: 1) directly scaling LLRs and 2) scaling pairwise substitution error rates, which changes the magnitude of LLRs. We also suggest the relation between substitution error rate and scaling values in the strategies by using curve fitting methods. Simulation results show that the error correcting performance from the proposed LLR calculation is better than that from the conventional scheme. Finally, we verify that the proposed LLR methods can be generally applied in DNA storage systems, and present practical methods to calculate error rates.