For data streaming applications, existing solutions are not yet able to close the gap between high data rates and low delay. This work considers the problem of data streaming under mixed delay constraints over a single communication channel with delayed feedback. We propose a novel layered adaptive causal random linear network coding (LAC-RLNC) approach with forward error correction. LAC-RLNC is a variable-to-variable coding scheme, i.e., variable recovered information data at the receiver over variable short block length and rate is proposed. Specifically, for data streaming with base and enhancement layers of content, we characterize a high dimensional throughput-delay trade-off managed by the adaptive causal layering coding scheme. The base layer is designed to satisfy the strict delay constraints, as it contains the data needed to allow the streaming service. Then, the sender can manage the throughput-delay trade-off of the second layer by adjusting the retransmission rate a priori and posterior as the enhancement layer, that contains the remaining data to augment the streaming service's quality, is with the relax delay constraints. We numerically show that the layered network coding approach can dramatically increase performance. We demonstrate that LAC-RLNC compared with the non-layered approach gains a factor of three in mean and maximum delay for the base layer, close to the lower bound, and factor two for the enhancement layer.
翻译:对于数据流应用,现有解决方案尚无法弥合数据流率高与延迟率低之间的差距。 这项工作考虑到数据流在单一通信频道的混合延迟限制和延迟反馈下存在数据流的问题。 我们建议采用新的分层因果随机线性网络编码(LAC-RLNC)方法,进行前向错误校正。 LAC-RLNC是一个可变可变的编码办法,即:在可变短区段长度和速率上将接收者回收的信息数据调整为前端和后端的可变编码。 具体地说,对于含有基础和增强内容层数据流的数据流,我们描述的是由适应性因果层编码办法管理的高度过量-交替交易问题。 基层设计的目的是满足严格的延迟限制,因为它包含允许流服务所需的数据。 然后,发送者可以通过调整前方和后方层的再传输率,将剩余数据流数据流数据流增加服务质量,我们从数字层中看出, 升级的网络升级率将大幅提升至下层,我们用下层计算出, 升级的升级为升级的升级为升级的升级的升级的网络方法可大幅提升。