📝 Real time interactive audio video in networks (29 MCQs)

📖 Explanation: The compression ratio is calculated by dividing the original size by the compressed size. Performing the division $\frac{26}{16}=1.625$ yields roughly 1.62, which corresponds to option A. The other numbers either represent a ratio of one, a value less than one, or an exaggerated figure, none of which match the computed result.

📖 Explanation: RLC replaces each block of identical symbols with the number of occurrences and the symbol itself. The sequence AAAA becomes 4A, BBB becomes 3B, and CC becomes 2C, producing the concatenated result 4A3B2C. Options B, C, and D either misplace the count or the symbol, making A the accurate encoding.

📖 Explanation: Total latency is the sum of each stage’s delay: $40\text{ ms}+30\text{ ms}+20\text{ ms}=90\text{ ms}$. This cumulative value represents the round‑trip time from capture to display, matching option A. The other options either overestimate or underestimate the combined delays, leading to incorrect totals.

📖 Explanation: RLC simply records the length of repeated symbols and does not maintain a dictionary of substrings; LZW, on the other hand, creates and expands a dictionary of previously seen sequences during encoding. Therefore option A correctly captures this fundamental difference, while the other statements mischaracterize one or both techniques.

📖 Explanation: Run‑length coding excels when long runs of identical pixels occur, which is common in frames with high spatial redundancy. It compresses those runs more effectively than LZW, which builds a dictionary but may not capture long runs as succinctly. Hence option B reflects the practical advantage of RLC in this scenario.

📖 Explanation: LZW builds a dictionary that can produce longer codewords as the dictionary grows, which may increase the average bits per symbol compared to Huffman coding that assigns shorter codes to more frequent symbols. Consequently, LZW often results in a slightly higher bandwidth demand, making option A the correct assessment.

📖 Explanation: Lossless compression ensures that the original data can be perfectly recovered, but it does not guarantee any specific reduction ratio. In practice, many audio segments compress less than 50 % of their original size, especially when the signal contains high entropy. Therefore option C, which asserts a universal 50 % reduction, is false, whereas the guarantee of exact reconstruction aligns with option A.

📖 Explanation: LZW adds a new entry consisting of the previously matched sequence plus the next character read from the input. After emitting the code for \AB\, the encoder appends the next character (say, \C\) to form \ABC\ as a new dictionary entry. This behavior matches option B, whereas the other choices misrepresent the dictionary update rule.

📖 Explanation: Shannon's entropy sets a lower bound on the average number of bits required to represent a source without loss. Any lossless algorithm that attempts to encode below this bound would necessarily discard information, violating the definition of losslessness. Hence option C correctly explains why respecting the entropy limit is essential.

📖 Explanation: A dynamic dictionary grows by learning new symbol sequences as they appear, allowing the encoder to capture emerging patterns in video content. This adaptability leads to better compression over time, especially when scenes change. Option B captures this benefit, while the other statements either misstate the behavior or describe unrealistic constraints.

📖 Explanation: In environments with fluctuating bandwidth, a codec that encodes quickly and can be re‑parameterized in real time (e.g., run‑length coding) helps maintain low latency while still providing some compression. High‑complexity codecs would introduce unacceptable delays, and freezing or sending raw video would either degrade interactivity or overwhelm the link. Thus option A offers the most balanced approach.

📖 Explanation: LZW is named after its inventors: Abraham Lempel, Jacob Ziv, and Terry Welch, who refined the original Lempel‑Ziv algorithm. The correct expansion is Lempel‑Ziv‑Welch, corresponding to option A. The other choices are fabricated and do not reflect the historical naming.

📖 Explanation: Standard RLC implementations often store the run length in a single byte, allowing counts from 0 to 255. This choice balances simplicity and sufficient range for many applications. While larger or variable‑length counters exist, the most common block size is one byte, making option A the correct answer.

📖 Explanation: Run‑length coding relies on long sequences of identical symbols to achieve high compression. When repetitions diminish, the algorithm must encode many short runs, which adds overhead and reduces overall compression efficiency. Consequently, the compression ratio falls, aligning with option A.

📖 Explanation: Most LZW implementations impose a limit on dictionary growth to bound memory usage. Upon reaching this limit, the encoder typically clears the dictionary and begins anew, preserving synchronization with the decoder. This reset behavior is captured in option A; the other alternatives describe uncommon or impractical responses.

📖 Explanation: The cumulative delay equals the sum of each stage: $25\text{ ms}+45\text{ ms}+20\text{ ms}=90\text{ ms}$. This total matches option A. Any other choice either adds or subtracts time incorrectly, leading to an inaccurate latency estimate.

📖 Explanation: Run‑length coding excels when data contains long runs of the same value, such as silence in audio or uniform areas in video. LZW, by building a dictionary of recurring sequences, is advantageous for video frames that exhibit repeated texture or motion patterns. Option A captures this nuanced suitability, whereas the other statements overgeneralize or mischaracterize the algorithms.

📖 Explanation: Real‑time streaming demands that encoding and decoding happen within tight timing constraints. LZW’s dynamic dictionary updates consume processing cycles; therefore, the most pivotal trade‑off is between the extra bits saved and the additional CPU workload needed to maintain the dictionary. Option C correctly highlights this balance, while the other options either ignore latency or describe less relevant concerns.

📖 Explanation: Intra‑frame JPEG requires discrete cosine transforms and quantization, which are computationally intensive and can increase processing delay. LZW, while involving dictionary look‑ups, generally incurs less computational overhead, leading to lower latency. Therefore option D, stating that JPEG reduces latency more than LZW, is false; the correct assessment is that LZW usually yields lower latency, aligning with option A. However, since the question asks for the comparison, option D is the only one that correctly identifies the opposite relationship, making it the answer.

📖 Explanation: Shannon's entropy sets a theoretical lower bound on the average number of bits required to represent data without loss. Any lossless scheme that attempts to compress below this bound would inevitably discard information, violating losslessness. Consequently, option C accurately reflects why the entropy limit cannot be undercut.

📖 Explanation: A dynamic dictionary expands as new byte sequences appear, allowing the encoder to capture emerging patterns in video frames. This adaptability improves compression efficiency as the content evolves, which is precisely described in option B. The other options either contradict the dynamic nature or impose unnecessary restrictions.

📖 Explanation: Variable bandwidth demands a codec that can quickly adapt its bitrate and incurs minimal processing delay. Run‑length coding offers fast encoding and can be retuned in real time, satisfying both bandwidth and latency constraints. High‑complexity codecs would introduce unacceptable latency, while pausing or sending raw video would either disrupt interactivity or overload the link. Hence option A best matches the required strategy.

📖 Explanation: Doubling the frame rate doubles the number of frames that must be sent each second. Even with identical compression efficiency per frame, the total amount of data per second roughly doubles, leading to a higher bitrate requirement. Option D incorrectly suggests no clear relationship, whereas option A correctly describes the expected increase.

📖 Explanation: Run‑length coding transmits counts and symbols together; if a packet containing a count is lost, the decoder cannot reconstruct the length of that run, potentially leading to gaps or artifacts. However, because each run is independent, the error typically remains localized, and the rest of the stream continues correctly. Option B best reflects this limited impact, whereas the other choices either overstate resilience or misstate retransmission behavior.

📖 Explanation: The LZW algorithm builds a dictionary of previously seen sequences; as the codebook grows, it can encode longer recurring patterns with single codes, typically enhancing compression. Therefore, option A correctly describes the positive correlation. Choices B and D misrepresent the effect, and option C incorrectly asserts that smaller dictionaries improve compression.

📖 Explanation: A static dictionary is predetermined and does not change as data flows, whereas a dynamic dictionary, as used in LZW, is constructed on‑the‑fly, adapting to the actual content. This distinction is captured by option A. The other statements invert or confuse the characteristics of the two methods.

📖 Explanation: Starting with a bigger dictionary means the encoder must send a larger set of initial code definitions, which can delay the beginning of actual media transmission. Consequently, option A correctly identifies the latency increase, while the other options either claim the opposite effect or ignore the overhead.

📖 Explanation: Lossless compression maintains the original data fidelity, which includes precise timing information, thereby helping keep sender and receiver in sync. While lossless techniques may add some processing delay, they do not inherently cause synchronization drift. Option B incorrectly states that lossless always improves synchronization, making it the false statement; the correct distinction is described in option A.

📖 Explanation: The bandwidth‑delay product equals bandwidth multiplied by latency: $2\text{ Mbps} \times 0.15\text{ s}=0.3\text{ Mbit}=300\text{ kbits}$. This value represents the amount of data that can be in transit simultaneously, matching option B. The other options either misuse units or miscalculate the product.