This article focuses on the compressed representations of pictures. The representation does not affect how many bits get from the Web server to the laptop, but it determines the usefulness of the bits that arrive. Many different representations are possible, and there is more involved in their choice than merely selecting a compression ratio. The techniques presented represent a single information source with several chunks of data ("descriptions") so that the source can be approximated from any subset of the chunks. By allowing image reconstruction to continue even after a packet is lost, this type of representation can prevent a Web browser from becoming dormant.

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Multiple description coding: compression meets the network

The present invention relates to a system for (1) protecting the transmission of packet streams between a host computer and one or more client computers, and (2) upon reception, (a) reconstructing any outage damage caused during the transmission to the packet streams, and (b) digitally recording the reconstructed packet streams to a file. The present invention also relates to a method for dynamically generating a file index table as the packet stream is being digitally recorded.

System for protecting the transmission of live data streams, and upon reception, for reconstructing the live data streams and recording them into files

Due to the explosive growth of the Internet and increasing demand for multimedia information on the Web, streaming video over the Internet has received tremendous attention from academia and industry. Transmission of real-time video typically has bandwidth, delay, and loss requirements. However, the current best-effort Internet does not offer any quality of service (QoS) guarantees to streaming video. Furthermore, for video multicast, it is difficult to achieve both efficiency and flexibility. Thus, Internet streaming video poses many challenges. In this article we cover six key areas of streaming video. Specifically, we cover video compression, application-layer QoS control, continuous media distribution services, streaming servers, media synchronization mechanisms, and protocols for streaming media. For each area, we address the particular issues and review major approaches and mechanisms. We also discuss the tradeoffs of the approaches and point out future research directions.

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Streaming video over the Internet: approaches and directions

A multiple-access MIMO WLAN system that employs MIMO, OFDM, and TDD. The system (1) uses a channel structure with a number of configurable transport channels, (2) supports multiple rates and transmission modes, which are configurable based on channel conditions and user terminal capabilities, (3) employs a pilot structure with several types of pilot (e.g., beacon, MIMO, steered reference, and carrier pilots) for different functions, (4) implements rate, timing, and power control loops for proper system operation, and (5) employs random access for system access by the user terminals, fast acknowledgment, and quick resource assignments. Calibration may be performed to account for differences in the frequency responses of transmit/receive chains at the access point and user terminals. The spatial processing may then be simplified by taking advantage of the reciprocal nature of the downlink and uplink and the calibration.

MIMO WLAN system

This paper addresses the problem of streaming packetized media over a lossy packet network in a rate-distortion optimized way. We show that although the data units in a media presentation generally depend on each other according to a directed acyclic graph, the problem of rate-distortion optimized streaming of an entire presentation can be reduced to the problem of error-cost optimized transmission of an isolated data unit. We show how to solve the latter problem in a variety of scenarios, including the important common scenario of sender-driven streaming with feedback over a best-effort network, which we couch in the framework of Markov decision processes. We derive a fast practical algorithm for nearly optimal streaming in this scenario, and we derive a general purpose iterative descent algorithm for locally optimal streaming in arbitrary scenarios. Experimental results show that systems based on our algorithms have steady-state gains of 2-6 dB or more over systems that are not rate-distortion optimized. Furthermore, our systems essentially achieve the best possible performance: the operational distortion-rate function of the source at the capacity of the packet erasure channel.

/pdf/rate-distortion-optimized-streaming-of-packetized-media-4eovs7229q.pdf

Rate-distortion optimized streaming of packetized media

A system and method for correcting errors and losses occurring during a receiver-driven layered multicast (RLM) of real-time media over a heterogeneous packet network such as the Internet. This is accomplished by augmenting RLM with one or more layers of error correction information. This allows each receiver to separately optimize the quality of received audio and video information by subscribing to at least one error correction layer. Ideally, each source layer in a RLM would have one or more multicasted error correction data streams (i.e., layers) associated therewith. Each of the error correction layers would contain information that can be used to replace lost packets from the associated source layer. More than one error correction layer is proposed as some of the error correction packets contained in the data stream needed to replace the packets lost in the associated source stream may themselves be lost in transmission. A preferred process for generating the error correction streams involves the use of a unique adaptation of the Forward Error Correction (FEC) techniques. This process encodes the transmission data using a linear transform which adds redundant elements. The redundancy permits losses to be corrected because any of the original data elements can be derived from any of the encoded elements. Thus, as long as enough of the encoded data elements are received so as to equal the number of the original data elements, it is possible to derive all the original elements.

Receiver-driven layered error correction multicast over heterogeneous packet networks

A video encoding system encodes video streams for multiple bit rate video streaming using an approach that permits the encoded bit rate to vary subject to a peak bit rate and average bit rate constraints for higher quality streams, while a bottom bit rate stream is encoded to achieve a constant chunk rate. The video encoding system also dynamically decides an encoding resolution for segments of the multiple bit rate video streams that varies with video complexity so as to achieve a better visual experience for multiple bit rate streaming.

Multiple bit rate video encoding using variable bit rate and dynamic resolution for adaptive video streaming

We consider the problem of error control for receiver-driven layered multicast of audio and video over the Internet. The sender injects into the network multiple source layers and multiple channel coding (parity) layers, some of which are delayed relative to the source, Each receiver subscribes to the number of source layers and the number of parity layers that optimizes the receiver's quality for its available bandwidth and packet loss probability. We augment this layered FEC system with layered pseudo-ARQ. Although feedback is normally problematic in broadcast situations, ARQ can be simulated by having the receivers subscribe and unsubscribe to the delayed parity layers to receive missing information. This pseudo-ARQ scheme avoids an implosion of repeat requests at the sender and is scalable to an unlimited number of receivers, We show gains of 4-18 dB on channels with 20% loss over systems without error control and additional gains of 1-13 dB when FEC is augmented by pseudo-ARQ in a hybrid system, Optimal error control in the hybrid system is achieved by an optimal policy for a Markov decision process.

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Error control for receiver-driven layered multicast of audio and video

Real-time communication (RTC) applications such as VoIP, video conferencing, and online gaming are flourishing. To adapt and deliver good performance, these applications require accurate estimations of short-term network performance metrics, e.g., loss rate, one-way delay, and throughput. However, the wide variation in mobile cellular network performance makes running RTC applications on these networks problematic. To address this issue, various performance adaptation techniques have been proposed, but one common problem of such techniques is that they only adjust application behavior reactively after performance degradation is visible. Thus, proactive adaptation based on accurate short-term, fine-grained network performance prediction can be a preferred alternative that benefits RTC applications. In this study, we show that forecasting the short-term performance in cellular networks is possible in part due to the channel estimation scheme on the device and the radio resource scheduling algorithm at the base station. We develop a system interface called PROTEUS, which passively collects current network performance, such as throughput, loss, and one-way delay, and then uses regression trees to forecast future network performance. PROTEUS successfully predicts the occurrence of packet loss within a 0.5s time window for 98% of the time windows and the occurrence of long one-way delay for 97% of the time windows. We also demonstrate how PROTEUS can be integrated with RTC applications to significantly improve the perceptual quality. In particular, we increase the peak signal-to-noise ratio of a video conferencing application by up to 15dB and reduce the perceptual delay in a gaming application by up to 4s.

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PROTEUS: network performance forecast for real-time, interactive mobile applications

An audio decoder provides a combination of decoding components including components implementing base band decoding, spectral peak decoding, frequency extension decoding and channel extension decoding techniques. The audio decoder decodes a compressed bitstream structured by a bitstream syntax scheme to permit the various decoding components to extract the appropriate parameters for their respective decoding technique.

Sanjeev Mehrotra

Papers

Receiver-driven layered error correction multicast over heterogeneous packet networks

Multiple bit rate video encoding using variable bit rate and dynamic resolution for adaptive video streaming

Error control for receiver-driven layered multicast of audio and video

PROTEUS: network performance forecast for real-time, interactive mobile applications

Bitstream syntax for multi-process audio decoding