Video streaming, in particular, hypertext transfer protocol based (HTTP) adaptive streaming (HAS) of video, is expected to be a dominant application over mobile networks in the near future. The observation that the base station can alter the video quality requested by a HAS client to its server by controlling the over-the-air throughput from the base station to the client implies that the base station can jointly maximize aggregate video quality of all the HAS flows and throughput of data flows that it serves. We formulate a utility maximization problem that separately takes into account different utility functions for video and data flows and show that the utility maximization can be achieved through an algorithm, we term adaptive guaranteed bit rate (AGBR), wherein target bit rates are calculated for each HAS flow and passed on to an underlying minimum rate proportional fair scheduler that schedules resources across all the flows. This approach has the advantage that it retains the existing scheduling function in the base station with a separate function to compute the target bit rates for the video flows allowing them to only change slowly over time in order to avoid frequent video quality changes. Through analytical modeling and simulations we show that the proposed algorithm can achieve required fairness among the video flows as well as automatically and fairly adapt video quality with increasing congestion thereby preventing data flow throughput starvation.

HTTP adaptive streaming (HAS) is quickly becoming a popular mechanism for delivering on-demand video content over the Internet. The chunked transmission and application-layer adaptation create a very different traffic pattern than traditional progressive video downloads where the entire video is downloaded with a single request.

In a multipath communication channel, the optimal receiver is matched to the maximum likelihood (ML) estimate of the multipath signal. In general, this leads to a computationally intensive multi-dimensional nonlinear optimisation problem. We develop a detection algorithm that avoids the ML estimation while still achieving good performance. Our approach is based on a geometric interpretation of the problem. The ML estimate of the multipath signal is the orthogonal projection of the received signal on a suitable signal subspace S. We design a second subspace G, the representation subspace, that is close to S, but whose orthogonal projection is easily computed. The "closeness" is measured by the gap metric. The subspace G is designed by using wavelet multiresolution analysis tools coupled with a reshaping algorithm in the Zak transform domain. We show an example where our approach significantly outperforms the correlator receiver and an alternative suboptimal approach.

The goal of the paper is to derive an approach for designing nearly shiftable scaling functions for multiresolution analyses (MRAs). Because this method does not increase the sampling density, the sparseness and efficiency of a dyadic grid is preserved. It contrasts with other attempts for the same problem which suffer either from oversampling or from being computationally expensive and data dependent. The algorithm reshapes a starting scaling function by modifying the Zak transform of its energy spectral density (ESD). The paper shows that although the modified signal does not strictly satisfy the 2-scale equation, the approximation error is sufficiently small. The result is a wavelet representation whose subband energy is "nearly" invariant to translations of its input. The paper illustrates this property with specific examples.