Video streaming is an increasingly popular way to consume media content. Adaptive video streaming is an emerging delivery technology which aims to increase user QoE and maximise connection utilisation. Many implementations naively estimate bandwidth from a one-sided client perspective, without taking into account other devices in the network. This behaviour results in unfairness and could potentially lower QoE for all clients. We propose an OpenFlow-assisted QoE Fairness Framework that aims to fairly maximise the QoE of multiple competing clients in a shared network environment. By leveraging a Software Defined Networking technology, such as OpenFlow, we provide a control plane that orchestrates this functionality. The evaluation of our approach in a home networking scenario introduces user-level fairness and network stability, and illustrates the optimisation of QoE across multiple devices in a network.

[1] We used the approach of conditional nonlinear optimal perturbation (CNOP) to investigate optimal precursors for El Nino-Southern Oscillation (ENSO) events with a theoretical coupled ocean-atmosphere model. The CNOPs of the annual cycle of the coupled system were computed for different time periods, and the derived CNOPs were compared with the linear singular vectors (LSVs). The results show the existence of the CNOPs of annual cycle and local CNOPs. These CNOPs have the robust optimal patterns, which have opposite polarities in sea surface temperature and thermocline depth anomalies in the eastern equatorial Pacific. We demonstrate that the CNOP (local CNOP), rather than LSVs, has the highest likelihood to develop into an El Nino (La Nina) event; thus the CNOPs (local CNOPs) can be regarded as the optimal precursors for El Nino (La Nina) events. These optimal precursors agrees qualitatively well with the observations of period of 1980-2002. On the basis of the nonlinear oscillation described by the model, the physical mechanism of the optimal precursors for ENSO is discussed.

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Seasonal dependence of initial error growth for El Niño‐Southern Oscillation (ENSO) in Zebiak‐Cane model is investigated by using a new approach, i.e. conditional nonlinear optimal perturbation (CNOP). It is found that CNOP‐type error tends to have a significant season‐dependent evolution, and produces most considerable negative effects on the forecast results. Therefore, CNOPs are closely related to spring predictability barrier (SPB). On the other hand, some other kinds of initial errors, whose patterns are different from those of CNOPs, have also been found. Although the magnitudes of such initial errors are the same as those of CNOPs in terms of the chosen norm, they either show less prominent season‐dependent evolutions, or have trivial effect on the forecast results, and consequently do not yield SPB for El Niño events. The results of this investigation suggest that the CNOP‐type errors can be considered as one of candidate errors that cause the SPB. If data assimilation or (and) targeting observation approaches possess the function of filtering the CNOP‐type or (and) other similar errors, it is hopeful to improve the prediction skill of ENSO.

[1] Most state-of-the-art climate models have difficulty in the prediction of El Niño-Southern Oscillation (ENSO) starting from preboreal spring seasons. The causes of this spring predictability barrier (SPB) remain elusive. With a theoretical ENSO system model, we investigate this controversial issue by tracing the evolution of conditional nonlinear optimal perturbation (CNOP) and by analyzing the behavior of initial error growth. The CNOPs are the errors in the initial states of ENSO events, which have the biggest impact on the uncertainties at the prediction time under proper physical constraints. We show that the evolution of CNOP-type errors associated with El Niño episodes depends remarkably on season with the fastest growth occurring during boreal spring in the onset phase. There also exist other kinds of initial errors, which have either somewhat smaller growth rates or neutral ones during spring. However, for La Niña events, even if initial errors are of CNOP-type, the errors grow without significant seasonal dependence. These findings suggest that the SPB in this model results from combined effects of three factors: the annual cycle of the mean state, the structure of El Niño, and the pattern of the initial errors. On the basis of the error tendency equations derived from the model, we addressed how the combination of the three factors causes the SPB and proposed a mechanism responsible for the error growth in the model ENSO events. Our results help in clarifying the role of the initial error pattern in SPB, which may provide a clue for explaining why SPB can be eliminated by improving initial conditions. The results also illustrate a theoretical basis for improving data assimilation in ENSO prediction.

ENSO is the strongest interannual signal in the global climate system with worldwide climatic, ecological and societal impacts. Over the past decades, the research about ENSO prediction and predictability has attracted broad attention. With the development of coupled models, the improvement in initialization schemes and the progress in theoretical studies, ENSO has become the most predictable climate mode at the time scales from months to seasons. This paper reviews in detail the progress in ENSO predictions and predictability studies achieved in recent years. An emphasis is placed on two fundamental issues: the improvement in practical prediction skills and progress in the theoretical study of the intrinsic predictability limit. The former includes progress in the couple models, data assimilations, ensemble predictions and so on, and the latter focuses on efforts in the study of the optimal error growth and in the estimate of the intrinsic predictability limit.

Within the Zebiak‐Cane model, we identify two types of initial errors that have significant season‐dependent evolutions related to the spring predictability barrier (SPB) for El Niño events. One type includes the sea surface temperature anomaly (SSTA) errors that have a zonal dipolar pattern with positive anomalies in the central equatorial Pacific and negative ones in the eastern equatorial Pacific; the other type consists of the SSTA errors with a spatial structure opposite to that of the former type, the zonal dipolar pattern shows negative anomalies in the central equatorial Pacific and positive anomalies in the eastern equatorial Pacific. The patterns of these two types of errors are nearly opposite of each other. The former causes the El Niño event to be underpredicted, and the latter causes the El Niño event to be overpredicted. For strong El Niño events the former tends to have a larger effect on the predictions than the latter, but for weak El Niño events, it is very difficult to determine which type of initial errors results in worse predictions. It is thought that strong (weak) El Niño events could be affected by strong (weak) nonlinearities. There are also other initial errors; however, they do not yield considerable season‐dependent evolutions nor can a common characteristic be extracted from their patterns. The two types of initial errors suggest two dynamical behaviors of error growth related to the SPB: in one case, the initial errors grow in a manner similar to the El Niño events; in the other case, the initial errors develop with a tendency opposite to the El Niño events. The two types of initial errors may capture the errors that exhibit significant season‐dependent evolutions related to the SPB. In addition, they may provide information regarding the “sensitive area” of ENSO predictions because of their localized regions. Therefore, if these types of initial errors exist in the realistic El Niño–Southern Oscillation (ENSO) predictions and if a data assimilation or a target method can filter them, the ENSO forecast skill may be improved. For ensemble forecast studies, different signs of prediction errors caused by the two types of initial errors could illustrate why the ensemble mean offers a better forecast than a single prediction.

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