Technology to provide link aware streaming adaptation is disclosed. In an example, a mobile device can include computer circuitry configured to: receive a manifest file for an HTTP adaptive stream from a node; determine a physical layer goodput of the mobile device with the node; and select a representation in the manifest file for a selected period based, at least in part, on the physical layer goodput.

Link-aware streaming adaptation

The wide adoption of multimedia service-capable mobile devices, the availability of better networks with higher bandwidths, and the availability of platforms offering digital content has led to an increasing popularity of multimedia streaming services. However, multimedia streaming services can be subject to different factors that affect the quality perceived by the users, such as service interruptions or quality oscillations due to changing network conditions, particularly in mobile networks. Dynamic Adaptive Streaming over HTTP (DASH), leverages the use of content-distribution networks and the capabilities of the multimedia devices to allow multimedia players to dynamically adapt the quality of the media streaming to the available bandwidth and the device characteristics. While many elements of DASH are standardized, the algorithms providing the dynamic adaptation of the streaming are not. The adaptation is often based on the estimation of the throughput or a buffer control mechanism. In this paper, we present a new throughput estimation adaptation algorithm based on a statistical method named Adaptive Forgetting Factor (AFF). Using this method, the adaptation logic is able to react appropriately to the different conditions of different types of networks. A set of experiments with different traffic profiles show that the proposed algorithm improves video quality performance in both wired and wireless environments.

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DASH Adaptation Algorithm Based on Adaptive Forgetting Factor Estimation

The Internet relies on congestion control protocols and adaptive applications that adjust their data rate to achieve good performance while avoiding network congestion. An essential prerequisite is the estimation of available network resources: implicitly like prevailing TCP versions that adapt their data rate iteratively; or explicitly by available bandwidth estimation techniques, as recently also adopted by TCP HyStart. Using observations of TCP throughput, applications like MPEG- DASH adapt the video quality and data rate. We discover, however, relevant conditions where TCP throughput is not a good bandwidth estimator and observe that it is outperformed by known UDP-based active probing methods. We investigate how the theory of active probing can possibly be used to extract relevant information also from passive TCP measurements. In case of TCP, the additional difficulty is found to be due to its chaotic traffic characteristics. We define a criterion to select relevant traffic samples and apply a regression technique to estimate the available bandwidth. Noteworthy, using the feedback provided by TCP acknowledgements, we can perform the estimation from sender-side measurements only. We verify the fidelity of the approach in a variety of experiments, including different types of cross-traffic, delays, and loss of data packets as well as acknowledgements.

Available bandwidth estimation from passive TCP measurements using the probe gap model

Dynamic adaptive streaming via HTTP (DASH) has been popular disseminated over the Internet especially under the circumstances of the time varying network, which it is currently the most challenging for providing smoothly video streaming via high quality. In DASH system, after completing the download of one segment, the player estimates the available network bandwidth by calculating the downloading throughput and then adapting the video bitrate level based on its estimations. However, the estimated bandwidth in the application layer is not accurate due to off-intervals appearance during the downloading process. To avoid the unfairness of bandwidth estimation by the clients, this work proposes a logarithmic approach for received network bandwidth, which includes increasing or decreasing this bandwidth logarithmically to converge the fair share bandwidth (estimated bandwidth). After obtaining the measured bandwidth, an adaptive bitrate algorithm is proposed by considering this measured bandwidth in addition to video buffer occupancy. The video buffer model is associated with three thresholds (i.e. one for initial startup and two for operating thresholds). When the video buffer’s level stays between the two operating thresholds, the video bitrate will keep unchanged. Otherwise, when the buffer occupancy is too high or too low, an appropriate video bitrate is chosen to avoid buffer overflow/underflow. Simulation results show that the proposed scheme is able to converge the measured bandwidth to the fair share bandwidth very quickly. Also the proposed scheme is compared with conventional scheme, we found that our proposed scheme outperforms in achieving the best performance in terms of efficiency, stability and fairness.

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Proposed an Adaptive Bitrate Algorithm based on Measuring Bandwidth and Video Buffer Occupancy for Providing Smoothly Video Streaming

Nowadays, Dynamic Adaptive Streaming over HTTP (DASH) has become very popular in streaming multimedia contents. In order to offer better user experience and cope with dynamically varying network conditions, a big challenge is to estimate precisely next segment bandwidth, which is a significant premise in determining next multimedia bitrate, especially when multiple clients share the same bottleneck link. In this paper, we address an innovative mechanism to estimate the bandwidth based on MACD indicator and its two thresholds. Then, with the estimated bandwidth, we use GA to determine the next multimedia bitrate. Our simulation demonstrates that our proposed approach outperforms other existing schemes.

Bandwidth Estimation Based on MACD for DASH

Nowadays, adaptive streaming is quite popular and is expected to develop at a high rate of speed Dynamic Adaptive Streaming over HTTP (DASH) is a new 3GPP and MPEG standard for adaptive steaming over HTTP which is currently being extensively accepted in the industry DASH requests best quality of media under available bandwidth with adaptive algorithm which is implemented in clients To obtain the appropriate quality of media, an accurate estimate of the bandwidth is needed In this paper, we present a novel algorithm to estimate the bandwidth even more accurately This bandwidth estimation algorithm involves bandwidth detective method and bandwidth computing method Outliers within a short term, slight fluctuations and persistent variations can be distinguished through our proposed bandwidth detective method Hereafter, we compute the estimated bandwidth in different methods based on the result of bandwidth detection We build a DASH platform and compare our proposed algorithm to the VLC adaptive algorithm According to the results, it is demonstrated that our algorithm possesses more accurate bandwidth estimation and higher buffer level Our proposed algorithm is sensitive to persistent variations and robust to slight fluctuations

Qi Lin

Papers

Bandwidth estimation of rate adaption algorithm in DASH