eCMT-SCTP: Improving Performance of Multipath SCTP with Erasure Coding Over Lossy Links

Performance of transport protocols on lossy links is a well-researched topic, however there are only a few proposals making use of the opportunities of erasure coding within the multipath transport protocol context. In this paper, we investigate performance improvements of multipath CMT-SCTP with the novel integration of the on-the-fly erasure code within congestion control and reliability mechanisms. Our contributions include: integration of transport protocol and erasure codes with regards to congestion control; proposal for a variable retransmission delay parameter (aRTX) adjustment; performance evaluation of CMT-SCTP with erasure coding with simulations. We have implemented the explicit congestion notification (ECN) and erasure coding schemes in NS-2, evaluated and demonstrated results of improvement both for application goodput and decline of spurious retransmission. Our results show that we can achieve from 10% to 80% improvements in goodput under lossy network conditions without a significant penalty and minimal overhead due to the encoding-decoding process

Random Linear Network Coding for Wireless Layered Video Broadcast: General Design Methods for Adaptive Feedback-free Transmission

This paper studies the problem of broadcasting layered video streams over heterogeneous single-hop wireless networks using feedback-free random linear network coding (RLNC). We combine RLNC with unequal error protection (UEP) and our main purpose is twofold. First, to systematically investigate the benefits of UEP+RLNC layered approach in servicing users with different reception capabilities. Second, to study the effect of not using feedback, by comparing feedback-free schemes with idealistic full-feedback schemes. To these ends, we study `expected percentage of decoded frames' as a key content-independent performance metric and propose a general framework for calculation of this metric, which can highlight the effect of key system, video and channel parameters. We study the effect of number of layers and propose a scheme that selects the optimum number of layers adaptively to achieve the highest performance. Assessing the proposed schemes with real H.264 test streams, the trade-offs among the users' performances are discussed and the gain of adaptive selection of number of layers to improve the trade-offs is shown. Furthermore, it is observed that the performance gap between the proposed feedback-free scheme and the idealistic scheme is very small and the adaptive selection of number of video layers further closes the gap.Comment: 15 pages, 12 figures, 3 tables, Under 2nd round of review, IEEE Transactions on Communication

Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery From Multiple Servers

In this paper, we deal with the problem of jointly determining the optimal coding strategy and the scheduling decisions when receivers obtain layered data from multiple servers. The layered data is encoded by means of prioritized random linear coding (PRLC) in order to be resilient to channel loss while respecting the unequal levels of importance in the data, and data blocks are transmitted simultaneously in order to reduce decoding delays and improve the delivery performance. We formulate the optimal coding and scheduling decisions problem in our novel framework with the help of Markov decision processes (MDP), which are effective tools for modeling adapting streaming systems. Reinforcement learning approaches are then proposed to derive reduced computational complexity solutions to the adaptive coding and scheduling problems. The novel reinforcement learning approaches and the MDP solution are examined in an illustrative example for scalable video transmission . Our methods offer large performance gains over competing methods that deliver the data blocks sequentially. The experimental evaluation also shows that our novel algorithms offer continuous playback and guarantee small quality variations which is not the case for baseline solutions. Finally, our work highlights the advantages of reinforcement learning algorithms to forecast the temporal evolution of data demands and to decide the optimal coding and scheduling decisions

Instantly Decodable Network Coding for Real-Time Scalable Video Broadcast over Wireless Networks

In this paper, we study a real-time scalable video broadcast over wireless networks in instantly decodable network coded (IDNC) systems. Such real-time scalable video has a hard deadline and imposes a decoding order on the video layers.We first derive the upper bound on the probability that the individual completion times of all receivers meet the deadline. Using this probability, we design two prioritized IDNC algorithms, namely the expanding window IDNC (EW-IDNC) algorithm and the non-overlapping window IDNC (NOW-IDNC) algorithm. These algorithms provide a high level of protection to the most important video layer before considering additional video layers in coding decisions. Moreover, in these algorithms, we select an appropriate packet combination over a given number of video layers so that these video layers are decoded by the maximum number of receivers before the deadline. We formulate this packet selection problem as a two-stage maximal clique selection problem over an IDNC graph. Simulation results over a real scalable video stream show that our proposed EW-IDNC and NOW-IDNC algorithms improve the received video quality compared to the existing IDNC algorithms

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

A Novel Framework to Select Intelligent Video Streaming Scheme for Learning Software as a Service

Cloud computing offers many benefits for government, business and educational institutions as exemplified in many cases. Options to deliver video streaming contents for educational purposes over cloud computing infrastructures are highlighted in this study. In such case, parameters that affect video quality directly or indirectly must be taken into account such as bandwidth, jitter and loss of data. Currently, several intelligent schemes to improve video streaming services have been proposed by researchers through different approaches. This study aims to propose a novel framework to select appropriate intelligent video streaming schemes for efficiently delivering educational video contents for Learning Software as a Service (LSaaS)