The main task of HTTP Adaptive Streaming is to adapt video quality dynamically under variable network conditions. This is a key feature for multimedia delivery especially when quality of service cannot be granted network-wide and, e.g., throughput may suffer short term fluctuations. Hence, robust bitrate adaptation schemes become crucial in order to improve video quality. The objective, in this context, is to control the filling level of the playback buffer and maximize the quality of the video, while avoiding unnecessary video quality variations. In this paper we study bitrate adaptation algorithms based on Backward-Shifted Coding (BSC), a scalable video coding scheme able to greatly improve video quality. We design bitrate adaptation algorithms that balance video rate smoothness and high network capacity utilization, leveraging both on throughput-based and buffer-based adaptation mechanisms. Extensive simulations using synthetic and real-world video traffic traces show that the proposed scheme performs remarkably well even under challenging network conditions.
Backward-Shifted Coding (BSC) @cite_12 and @cite_20 are two schemes which leverage on time redundancy mechanisms for scalable video coding. In both schemes, base layer or enhancement layers of any number of future segments of a video can be delivered in advance.
@cite_20 @cite_12
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Although HTTP-based video streaming can easily penetrate firewalls and profit from Web caches, the underlying TCP may introduce large delays in case of a sudden capacity loss. To avoid an interruption of the video stream in such cases we propose the Backward-Shifted Coding (BSC). Based on Scalable Video Coding (SVC), BSC adds a time-shifted layer of redundancy to the video stream such that future frames are downloaded at any instant. This pre-fetched content maintains a fluent video stream even under highly variant network conditions and leads to high Quality of Experience (QoE). We characterize this QoE gain by analyzing initial buffering time, re-buffering time and content resolution using the Ballot theorem. The probability generating functions of the playback interruption and of the initial buffering latency are provided in closed form. We further compute the quasi-stationary distribution of the video quality, in order to compute the average quality, as well as temporal variability in video quality. Employing these analytic results to optimize QoE shows interesting trade-offs and video streaming at outstanding fluency. The scalable video coding extension of H.264 AVC is a current standardization project. This paper deals with unequal error protection (UEP) scheme for scalable video bitstream over packet-lossy networks using forward error correction (FEC). The proposed UEP scheme is developed by exploiting jointly the unequal importance existing both in temporal layers and quality layers of hierarchial scalable video bitstream. For efficient assignment of FEC codes, the proposed UEP scheme uses a simple and efficient performance metric, namely layer-weighted expected zone of error propagation (LW-EZEP). The LW-EZEP is adopted for quantifying the error propagation effect on video quality degradation from packet loss in temporal layers and in quality layers. Compared to other UEP schemes, the proposed UEP scheme demonstrates strong robustness and adaptation for variable channel status. In existing pyramid-based spatially scalable coding schemes, such as H.264 MPEG-4 SVC (scalable video coding), video frame at a certain high-resolution layer is mainly predicted either from the same frame at the next lower resolution layer, or from the temporal neighboring frames within the same resolution layer. But these schemes fail to exploit both kinds of correlation simultaneously and therefore cannot remove the redundancies among resolution layers efficiently. This paper extends the idea of spatiotemporal subband transform and proposes a general in-scale motion compensation technique for pyramid-based spatially scalable video coding. Video frame at each high-resolution layer is partitioned into two parts in frequency. Prediction for the lowpass part is derived from the next lower resolution layer, whereas prediction for the highpass part is obtained from neighboring frames within the same resolution layer, to further utilize temporal correlation. In this way, both kinds of correlation are exploited simultaneously and the cross-resolution-layer redundancy can be highly removed. Furthermore, this paper also proposes a macroblock-based adaptive in-scale technique for hybrid spatial and SNR scalability. Experimental results show that the proposed techniques can significantly improve the spatial scalability performance of H.264 MPEG-4 SVC, especially when the bit-rate ratio of lower resolution bit stream to higher resolution bit stream is considerable. Layered video coding is capable of progressively refining the reconstructed video quality with the aid of multiple layers of unequal importance. When the base layer (BL) is corrupted or lost due to channel impairments, the enhancement layers (ELs) must be discarded by the video decoder, regardless whether they are perfectly decoded or not, which implies that the transmission power assigned to the ELs is wasted. To circumvent this problem, we proposed a bit-level inter-layer forward error correction (IL-FEC) scheme for layered video transmission in our previous work, which implanted the systematic bits of the BL into the systematic bits of the ELs using exclusive-OR operations (XOR). This allowed the receiver to exploit the implanted bits of the ELs for assisting the BL's decoding and hence improved the overall system performance of our IL-FEC aided layered video scheme. In this treatise, we find the specific FEC coding rates in a real-time on-line fashion for the sake optimizing the overall system performance. The proposed procedure is widely applicable to diverse wireless transceivers and FEC codecs. Our simulation results show that the proposed optimized IL-FEC system outperforms the traditional optimal UEP by about 1.9 dB of Eb N0 at a peak signal-to-noise ratio (PSNR) of 38 dB. Viewing the improvements in terms of the video quality, 3.3 dB of PSNR improvement is attained at an Eb N0 of 10 dB, when employing a recursive systematic convolutional (RSC) code.